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Timing and carrier synchronization in wireless communication systems: a survey and classification of research in the last 5 years

Abstract

Timing and carrier synchronization is a fundamental requirement for any wireless communication system to work properly. Timing synchronization is the process by which a receiver node determines the correct instants of time at which to sample the incoming signal. Carrier synchronization is the process by which a receiver adapts the frequency and phase of its local carrier oscillator with those of the received signal. In this paper, we survey the literature over the last 5 years (2010–2014) and present a comprehensive literature review and classification of the recent research progress in achieving timing and carrier synchronization in single-input single-output (SISO), multiple-input multiple-output (MIMO), cooperative relaying, and multiuser/multicell interference networks. Considering both single-carrier and multi-carrier communication systems, we survey and categorize the timing and carrier synchronization techniques proposed for the different communication systems focusing on the system model assumptions for synchronization, the synchronization challenges, and the state-of-the-art synchronization solutions and their limitations. Finally, we envision some future research directions.

Introduction

Motivation: The Wireless World Research Forum (WWRF) prediction of seven trillion wireless devices serving seven billion people by 2020 [1] sums up the tremendous challenge facing existing wireless cellular networks: intense consumer demand for faster data rates. Major theoretical advances, such as the use of multiple antennas at the transmitter and receiver (multiple-input multiple-output (MIMO)) [2, 3], orthogonal frequency-division multiple access (OFDMA) [4], and cooperative relaying [57] have helped meet some of this demand and have been quickly incorporated into communication standards. These technologies also form a core part of the next-generation cellular standards, 5G, which is under development [8, 9].

In order to fulfill the demand for higher data rates, a critical requirement is the development of accurate and realizable synchronization techniques to enable novel communication paradigms. Such synchronization techniques allow communication systems to deliver higher data rates, e.g., through the use of higher-order modulations and utilization of cooperative communication schemes. Hence, there has been considerable research recently in synchronization techniques for novel communication strategies.

Aim: The aim of this paper is to provide a survey and classification of the research in the field of synchronization for wireless communication systems that spans the last 5 years (2010–2014). This is not an easy task given the large number of papers dealing with synchronization and its associated challenges in both current and emerging wireless communication systems. The critical need for such a survey is highlighted by the fact that the last comprehensive survey paper on synchronization was published nearly a decade ago [10]. While survey papers on synchronization for wireless standardization have recently appeared [1113], these surveys do not overview the state-of-the-art published research. For an overview of the state-of-the-art in synchronization research prior to 2010, see the 2009 special issue on synchronization in wireless communications of the EURASIP Journal on Wireless Communications and Networking [14].

In this survey, we overview the relationships within the published research in terms of system model and assumptions, synchronization challenges, proposed methods, and their limitations. We also highlight future research directions and important open problems in the field of synchronization. The main intended audience of this survey paper are those interested in or already working in synchronization. This survey paper aims to enable researchers to quickly immerse themselves in the current state of the art in this field. Moreover, by highlighting the important open research issues and challenges, we believe the paper would stimulate further research in this field. Since this paper is not intended to be a tutorial on synchronization, we deliberately avoid presenting mathematical details and instead focus on the big picture.

Background and scope: Synchronization is a common phenomenon in nature, e.g., the synchronized flashing of fireflies or the synchronous firing of neurons in the human brain [15, 16]. In wireless communications, timing and carrier synchronization are fundamental requirements [17]. In general, a wireless receiver does not have prior knowledge of the physical wireless channel or propagation delay associated with the transmitted signal. Moreover, to keep the cost of the devices low, communication receivers use low-cost oscillators which inherently have some drift. In this context, timing synchronization is the process by which a receiver node determines the correct instants of time at which to sample the incoming signal and carrier synchronization is the process by which a receiver adapts the frequency and phase of its local carrier oscillator with those of the received signal. Particularly, depending on the specific communication systems, synchronization definition/procedure could be very different. According to 3rd Generation Partnership Project, a terminology referred to as cell search has been widely used to represent an entire synchronization procedure, which may constitute both initial and target cell searches. Timing synchronization may consist of frame/slot/symbol/chip synchronizations, residual timing tracking, first arrival path search (in terms of OFDMA), multi-path search (in terms of CDMA), etc. Similarly, carrier synchronization may imply integer/fractional frequency offset estimation (in terms of OFDMA), coarse/fine frequency offset estimation (in terms of CDMA), residual frequency offset tracking, etc.

Major advances in timing and carrier synchronization such as pilot-symbol-assisted modulation [18] are used in present-day cellular networks to achieve carrier accuracy of 50 parts per billion and timing accuracy of 1 μs (±500 ns) [11]. The requirement in future wireless networks is toward tighter accuracies, e.g., timing accuracy of 200 ns, to enable location-based services [12]. Hence, there is a need for new and more accurate timing and carrier estimators. In general, in order to quantify the performance of any proposed estimator, a lower bound on the mean-square estimation error can be derived. The bounds are also helpful in designing efficient training sequences. In addition, for multiple parameters needed, say, for the joint estimation of timing and carrier frequency offsets, these bounds include coupling information between the estimation of these parameters. For example, if the bound suggests very-low coupling between the estimation of timing and carrier frequency offsets, this implies that these parameters can be estimated separately without any significant loss in the estimation performance. In particular, there usually exists strong coupling between channel and carrier frequency offset estimation and their joint estimation is helpful to achieve improved estimation accuracy [19, 20].

Although timing and carrier synchronization are necessary for successful communication, they cannot provide a common notion of time across distributed nodes. Clock synchronization is the process of achieving and maintaining coordination among independent local clocks to provide a common notion of time across the network. Some wireless networks, such as worldwide interoperability for microwave access (WiMAX), are synchronized to the global positioning system (GPS) [12]. Others, e.g., Bluetooth, wireless fidelity (Wi-Fi), and Zigbee rely on a beacon strategy, where all nodes in the network follow the same time reference given by a master node broadcasting a reference signal [12]. For recent surveys on clock synchronization, please see [2125].

In the literature, timing and carrier synchronization techniques are sometimes considered in conjunction with radio frequency (RF) front-end impairments. RF impairments arise as a result of the intrinsic imperfections in many different hardware components that comprise the RF transceiver front ends, e.g., amplifiers, converters, mixers, filters, and oscillators. The three main types of RF impairments are I/Q imbalance, oscillator phase noise, and high-power amplifier (HPA) nonlinearities [26]. I/Q imbalance refers to the amplitude and phase mismatch between the in-phase (I) and quadrature (Q) signal branches, i.e., the mismatch between the real and imaginary parts of the complex signal. Oscillator phase noise refers to the noise in an oscillator, mainly due to the active devices in the oscillator circuitry, which introduces phase-modulated noise, directly affecting the frequency stability of the oscillator [27]. The HPA nonlinearities refer to the operation of the HPA in its nonlinear region when working at medium- and high-power signal levels. The influence of these RF impairments is usually mitigated by suitable compensation algorithms, which can be implemented by analog and digital signal processing. For a detailed discussion of RF impairments, the reader is referred to [28]. In cases where RF impairments (typically I/Q imbalance or phase noise) are considered in conjunction with timing and carrier synchronization, they are identified separately in the classification.

Methodology: Synchronization is generally considered as a subfield of signal processing. According to Google Scholar, nine out of the top ten publication avenues in signal processing are IEEE journals [29]. Hence, we used the IEEEXplore database to search for papers on timing and carrier synchronization. We selected papers (in December 2014) by searching for words “frequency offset” OR “frequency offsets” OR “timing offset” OR “timing offsets” in IEEEXplore metadata only. In order to focus on the important recent advances, we limited our search to all journal papers published in the last 5 years only, i.e., from 2010 to 2014. Also, we limited the search to the following conferences: ICC, GLOBECOM, VTC, WCNC, SPAWC, and PIMRC, because it was found that these conferences contained sufficient numbers of papers to address the synchronization topics.

Using these principles, papers that dealt with timing and carrier synchronization were carefully selected for inclusion in this survey paper. A classification of these papers, with respect to the adopted communication system, is presented in Table 1. Some papers were found to study the effect of timing and carrier synchronization on the performance of various communication systems, but they did not directly estimate or compensate for these synchronization impairments. These papers are summarized in Table 2 for the sake of completeness. However, these papers are not discussed in the survey sections below.

Table 1 Classification of papers on timing or carrier synchronization, 2010–2014
Table 2 Papers on the effect of timing or carrier synchronization on the system performance, 2010–2014

Abbreviations and acronyms: The list of abbreviations and acronyms used in this paper are detailed in Table 3. In the paper, in Tables 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, and 21, “CSI Req.” column indicates (using yes/no value) whether or not channel state information (CSI) is required for synchronization procedure; “CE” column indicates (using yes/no value) whether or not algorithm considers channel estimation (CE); “Est/Comp” column indicates whether algorithm only considers estimation (Est) of parameters or also uses the estimated parameters for compensating (Comp) their effect on system bit error rate (BER) performance; “N/A” stands for not applicable; and “Bound” column indicates whether the paper derives or provides lower bound on the estimation performance.

Table 3 List of common acronyms and abbreviations
Table 4 Summary of synchronization research in single-carrier SISO communication systems
Table 5 Summary of research in multi-carrier SISO communication systems considering carrier synchronization
Table 6 Summary of research in multi-carrier SISO communication systems considering timing synchronization
Table 7 Summary of research in multi-carrier SISO communication systems considering joint timing and carrier synchronization
Table 8 Summary of synchronization research in single-carrier multi-antenna communication systems
Table 9 Summary of synchronization research in multi-carrier multi-antenna communication systems
Table 10 Summary of synchronization research in single-carrier cooperative communication systems
Table 11 Summary of synchronization research in multi-carrier cooperative communication systems
Table 12 Summary of synchronization research in SC-FDMA uplink communication systems
Table 13 Summary of synchronization research in OFDMA uplink communication systems
Table 14 Summary of synchronization research in CDMA communication systems
Table 15 Summary of synchronization research in cognitive radio-based communication systems
Table 16 Summary of synchronization research in distributed multiuser communication systems
Table 17 Summary of synchronization research in CoMP-based communication systems
Table 18 Summary of synchronization research in multicell interference based communication systems
Table 19 Summary of synchronization research in UWB communication systems
Table 20 Summary of synchronization research in spread spectrum communication systems
Table 21 Summary of synchronization research in millimeter wave communication systems

Organization: The survey is organized as follows. The selected papers are classified into five categories: (i) SISO (Section 2), (ii) MIMO (Section 3), (iii) cooperative relaying (Section 4), (iv) multicell/multiuser (Section 5), and (v) others (ultra-wide band (UWB) and spread spectrum) communication networks (Section 6). Each category is split into single-carrier and multi-carrier (e.g., OFDM) systems. For each category, we discuss the system model for synchronization, the synchronization challenges, and the state-of-the-art synchronization solutions and their limitations. Future research directions and important open problems are highlighted in Section 7. Finally, Section 8 concludes this survey.

SISO systems

Single-carrier SISO communication systems

System model

In single-carrier SISO systems, a single antenna transmitter communicates with a single antenna receiver and the information is modulated over a single carrier.

The transmitter is assumed to communicate with the receiver through an additive white Gaussian noise (AWGN) or frequency-flat/frequency-selective fading channel. At the receiver end, the effect of channel can be equalized either in the time domain or the frequency domain. Time domain equalization is a simple single tap or multi-tap filter. In frequency domain equalization, also referred to as single-carrier frequency domain equalization (SC-FDE), frequency domain equalization is carried out via the fast Fourier transform (FFT) and inverse fast Fourier transform (IFFT) operations at the receiver.

Synchronization challenge

The received signal at the receiver is affected by a single timing offset (TO) and a single-carrier frequency offset (CFO). The receiver has to estimate these parameters and compensate for their effects from the received signal in order to decode it. The receiver may or may not have the knowledge of CSI. In case of no CSI availability, a receiver has to carry out CE in addition to TO or CFO estimation. The estimation of TO and CFO can be achieved using pilots or by blind methods. For pilot-based estimation, a transmitter sends a known training sequence (TS) to the receiver before sending the actual data. For blind estimation, a receiver estimates the synchronization parameters using unknown received data. Note that there exists coupling between channel and CFO estimation and their joint estimation is helpful to achieve the best estimation accuracy for these parameters [19, 20].

Literature review

The summary of the research carried out to achieve timing and carrier synchronization in single-carrier SISO communication systems is given in Table 4:

  1. 1.

    The estimation or compensation of timing offset alone and frequency offset alone is studied in [3050] and [5156], respectively.

  2. 2.

    Joint timing and carrier synchronization is studied in [5767].

The categorized papers differ in terms of channel model, channel estimation requirements, or pilot/training requirements. They also differ in whether proposing estimation, compensation, joint channel estimation, or estimating lower bound. Further details or differences among these papers are provided in the last column of Table 4, which further indicates whether any additional parameter such as phase noise (PHN), IQ imbalance, signal-to-noise ratio (SNR) estimation, or direction of arrival (DoA) estimation, is considered. Moreover, whether training sequence (TS) design or hardware implementation is taken into consideration is also labeled in this table.

Summary

Timing and carrier synchronization for single-carrier SISO communication systems is a very well-researched topic. The majority of the papers in Table 4 are published before 2012. Generally, it is not possible to identify the best pilot-based and best blind-based estimators since the papers have widely different system model assumptions. One possible future work in this area should compare the performance of their proposed solutions to existing work in Table 4 with similar assumptions to make clear how the state of the art is advancing.

Since it is desired to implement both estimation and compensation of timing and carrier frequency offsets, a few research works have considered such problems assuming AWGN [63, 64], frequency flat [61], and frequency selective channels [57]. The future work in this area may consider [57, 61, 63, 64] as baseline research work and further develop for more efficient estimation and compensation techniques and hardware implementation.

Multi-carrier SISO communication systems

System model

In multi-carrier systems, information is modulated over multiple carriers. The well-known multi-carrier system is based upon OFDM.1 In OFDM systems, at the transmitter side, an IFFT is applied to create an OFDM symbol and a cyclic prefix is appended to the start of an OFDM symbol. At the receiver, the cyclic prefix is removed and an FFT is applied to the received OFDM symbol. Note that frequency domain processing greatly simplifies receiver processing. The length of the cyclic prefix is designed to be larger than the span of the multipath channel. The portion of the cyclic prefix which is corrupted due to the multipath channel from the previous OFDM symbols is known as the inter-symbol interference (ISI) region. The remaining part of the cyclic prefix which is not affected by the multipath channel is known as the ISI-free region. Note that cyclic prefix can mainly remove the ISI and the proper design of the cyclic prefix length has been a design issue under research.

Synchronization challenge

In OFDM systems, the presence of TO affects the system performance in a different ways as compared to single-carrier systems:

  1. 1.

    If the TO lies within the ISI-free region of the cyclic prefix, the orthogonality among the subcarriers is not destroyed and the timing offset only introduces a phase rotation in every subcarrier symbol. For a coherent system, this phase rotation is compensated for by the channel equalization scheme, which views it as a channel-induced phase shift.

  2. 2.

    If the TO is outside the limited ISI-free region, the orthogonality among the subcarriers is destroyed by the resulting ISI and additional inter carrier interference (ICI) is introduced.

Thus, the objective of timing synchronization in OFDM systems, unlike in single-carrier systems, is to identify the start of an OFDM symbol within the ISI-free region of the cyclic prefix.

The presence of CFO in OFDM systems attenuates the desired signal and introduces ICI since the modulated carrier is demodulated at an offset frequency at the receiver side. In OFDM systems, CFO is usually represented in terms of subcarrier spacings and can be divided into an integer part (integer number less than the total number of subchannels) and a fractional part (within \(\pm \frac {1}{2}\) of subcarrier spacing). If the CFO is greater than the subcarrier spacing, a receiver has to estimate and compensate for both the integer and fractional parts of the normalized CFO.

The synchronization in OFDM systems can be performed either in the time domain or the frequency domain depending upon whether the signal processing is executed pre-FFT or post-FFT at the receiver, respectively.

Literature review

The summary of the research carried out to achieve carrier synchronization, timing synchronization, and joint timing and carrier synchronization in multi-carrier SISO communication systems is given in Tables 5, 6, and 7, respectively. Their details are given below.

  1. 1.

    Carrier synchronization:

    The papers studying carrier synchronization in multi-carrier SISO communication systems are listed in Table 5. It can be observed that there are groups of papers which consider the same channel model and the same requirement for CSI and training. Also, they consider the same problem in terms of estimation or compensation. In the following, we describe how these papers differ within their respective groups.

    1. (a)

      Pilot-based CFO estimation and compensation with channel estimation:

      The papers here can be grouped into two categories. The first group does not provide an estimation error lower bound [6884]. In addition to carrier synchronization, [68] proposes to achieve seamless service in vehicular communication and also considers road side unit selection; [69] considers CFO tracking assuming constant modulus-based signaling; [70] considers concatenated precoded OFDM system, [71] proposes MMSE-based estimation; [72] proposes hard-decision-directed-based CFO tracking; [73] considers phase-rotated conjugate transmission and receiver feedback; [74] considers hardware implementation with IQ imbalance and power amplifier nonlinearity; [75] considers hexagonal multi-carrier transmission system and a doubly dispersive channel; [76] considers maximum a posteriori expectation-maximization (MAP-EM)-based Turbo receiver; [77] considers an FBMC system; [78] considers aerial vehicular communication; [79] proposes noise variance estimation and considers EM algorithm; [80] considers SFO estimation; [81] considers hardware implementation, [82] proposes estimation of the CFO over a wide range of offset values; [83] considers IQ imbalance and phase noise distortion; and [84] considers Doppler spread in a mobile OFDM system.

      The second group of papers provides an estimation error lower bound on obtaining the CFO [8592]. In addition to carrier synchronization, [85] considers IQ imbalance, [87] proposes an extended Kalman filter (EKF)-based estimator in the presence of phase noise, [88] proposes an ML estimator and considers an FBMC system, [89] considers SFO estimation and synchronization, [90] considers ML based frequency tracking, and [91] considers IQ imbalance and its estimation.

    2. (b)

      Pilot-based CFO estimation with channel estimation:

      The papers [9395] fall under this category. In addition to carrier synchronization, [94] proposes computationally efficient, single training sequence-based least squares estimation, [93] considers doubly selective channel estimation, and [95] proposes an EM-based ML estimator and also considers the presence of phase noise.

    3. (c)

      Blind CFO estimation with no channel estimation:

      The papers here can be grouped into two categories. The first group does not provide an estimation lower bound [96101]. In addition to carrier synchronization, [96] considers a cognitive radio network and the algorithm applies even if timing offset is unknown, [97] considers time-varying channels and Doppler frequency, [98, 99] consider constant modulus-based signaling, [100] considers cyclic correlation-based estimation, the estimator proposed by [101] is based on minimum reconstruction error, and [92] proposes an EM based estimator considering very high mobility.

      The second group of papers provides an error lower bound on CFO estimation [102, 103]. In addition to carrier synchronization, [102] proposes a Viterbi-based estimator and [103] proposes CFO estimation using single OFDM symbol and provides closed-form expression for the CFO estimate using property of the cosine function.

    4. (d)

      Pilot-based CFO estimation with no channel estimation:

      The papers here can be grouped into two categories. The first group of papers does not provide an estimation error lower bound [104117]. In addition to carrier synchronization, the CFO estimation algorithm proposed by [104] is valid even if timing offset and channel length is unknown; the algorithm proposed by [105] estimates integer frequency offset; the algorithm proposed by [106] estimates sampling frequency offset in addition to CFO; [107] estimates IFO for OFDM-based digital radio mondiale plus system; [108] considers IQ imbalance and direct-conversion receivers; [109] considers CFO tracking in digital video broadcasting (DVB-T) system; [110] proposes ML-based estimation; [111, 113] propose IFO estimation with cell sector identity detection in Long-Term Evolution systems; [112] considers IQ imbalance and hardware implementation; [114] also considers SFO estimation; [115] proposes ML-based estimation and considers the design of pilot pattern, the estimation algorithm in [116] is robust to the presence of the Doppler shift; and [117] considers IQ imbalance and its estimation.

      The second group of papers provides an error lower bound on CFO estimation [118130]. In addition to carrier synchronization, [118] derives CRLB for the general case where any kind of subcarriers, e.g., pilot, virtual, or data subcarriers, may exist; [119] considers eigenvalue-based estimation; [120] considers subspace-based channel estimation with hardware implementation and SNR detection; [121] considers IFO estimation and training sequence design; [122] considers Gaussian particle filtering-based estimation; [123] proposes both IFO and FFO estimation while also considering IQ imbalance and a direct conversion receiver structure; [124] considers SFO estimation while proposing ML-based estimation; [125] proposes multiple signal classification or a subspace-based estimation method; [126] proposes an estimator based on the space-alternating generalized expectation-maximization (SAGE) algorithm and considers IQ imbalance; [127] proposes SNR and noise power estimation; [128, 129] consider IQ imbalance; and [130] considers doubly selective fading channels.

    5. (e)

      Pilot-based CFO estimation and compensation with no channel estimation:

      The papers [131134] fall under this category. In addition to carrier synchronization, [131] proposes training sequence design in DVB-T2 system, [132] considers frequency domain pilot signaling, [133] also considers SFO estimation, and [134] considers IQ imbalance and its estimation.

    6. (f)

      Pilot-based CFO compensation with no channel estimation:

      The papers [135138] fall under this category. The differences among them are that in addition to carrier synchronization, [136] proposes repeated correlative coding for mitigation of ICI, [135] proposes training sequence design, [137] considers cell identification in Long-Term Evolution (LTE) system, and [138] considers detection of primary synchronization signal in LTE systems.

    7. (g)

      Blind CFO compensation with no channel estimation:

      The papers [139142] fall under this category. The differences among them are that in addition to carrier synchronization, [139, 140] propose EKF-based algorithm and space time parallel cancelation schemes, respectively, to cancel out inter-carrier interference due to CFO, [141] proposes reduction of peak-interference-to-carrier ratio, and [142] considers IQ imbalance.

  2. 2.

    Timing synchronization:

    Compared to the categorized papers for carrier synchronization in Table 5, the categorized papers for timing synchronization in Table 6 have greater similarity. The major differences are found in terms of channel estimation requirement, pilot/training requirement, and lower bounds on the estimation performance. Further details are provided in the last column of Table 6, which also indicates if any additional parameter, e.g., SNR estimation, is considered.

  3. 3.

    Joint timing and carrier synchronization:

    The papers studying joint timing and carrier synchronization in multi-carrier SISO communication systems are listed in Table 7. It can be observed that there are groups of papers which consider the same channel model and the same requirement for CSI and training. Also, they further consider the same problem in terms of estimation or compensation. In the following, we describe how these papers differ within their respective groups.

    1. (a)

      Pilot-based TO and CFO estimation and compensation with channel estimation:

      The papers [143147] fall under this category. In addition to joint timing and carrier synchronization, [143] considers IFO estimation while considering residual timing offset, [144] considers hardware implementation, [145] considers FBMC system, [146] considers offset-QAM modulation, and [147] considers decision directed-based estimation.

    2. (b)

      Blind TO and CFO estimation with no channel estimation:

      The papers [148, 149] fall under this category. In addition to joint timing and carrier synchronization, [148] considers digital video broadcasting (DVB-T2) standard and [149] proposes ML estimation with a time-domain preamble.

    3. (c)

      Pilot-based TO and CFO estimation and compensation with no channel estimation:

      The papers [150, 151] fall under this category. In addition to joint timing and carrier synchronization, [150] considers time domain synchronous (TDS)-OFDM system which replaces cyclic prefix with a pseudo noise (PN) and thus proposes PN-correlation-based synchronization, and [151] considers hardware implementation.

    4. (d)

      Pilot-based TO and CFO estimation with no channel estimation:

      The first group of papers does not provide an estimation error lower bound [152155]. The differences among them are that in addition to joint timing and carrier synchronization, CFO estimation in [152] applies to a wide CFO range, i.e., ±1/2 the total number of subcarrier widths; [153] considers phase noise (PN)-sequence-based preamble; [154] considers digital video broadcasting (DVB-T2) system; and [155] considers blind cyclic prefix length in their algorithm.

      The second group of papers provides an estimation error lower bound [156158]. In addition to joint timing and carrier synchronization, [156] considers doubly selective channel, [157] considers hexagonal multi-carrier transmission system, and [158] proposes training sequence design.

Summary

Timing and carrier synchronization for multi-carrier SISO communication systems is still an ongoing topic of research, as evidenced by the large number of published papers. In particular, there is a major emphasis on accurate CFO estimation for different types of systems and often in conjunction with RF impairments such as phase noise and IQ imbalance.

Few recent research works have considered the practical problem of both estimation and compensation of timing and carrier frequency offsets in the presence of channel impairments [143147]. Particularly, [147] considers known symbol padding (KSP)-OFDM system, [143, 144] also introduces a hardware co-simulation platform, [145] considers an FMBC system, and [146] considers an OQAM-OFDM system. The authors in [143] only consider integer frequency offsets and the particular work may be extended considering both integer and fractional frequency offsets. The works of [143147] can be considered as a baseline reference for further extension in the relevant system models.

Multi-antenna systems

Single-carrier multi-antenna communication systems

System model

In a multi-antenna wireless communication system, data are transmitted across different channels that are modeled either as quasi-static or time varying. The received signal at an antenna is given by a linear combination of the data symbols transmitted from different transmit antennas. In order to achieve multiplexing or capacity gain, independent data are transmitted from different transmit antennas.

A space-time MIMO decoder can be used to decode the signal from multiple antenna streams. On the other hand, in order to achieve diversity gain, the same symbol weighted by a complex scale factor may be sent over each transmit antenna. This latter scheme is also referred to as MIMO beamforming [159]. Depending on the spatial distance between the transmit or receive antennas, which may differ for line-of-sight (LOS) and non-LOS propagation, the antennas may be equipped with either their own oscillators or use the same oscillator.

Depending on the number of antennas at the transmitter and the receiver, multi-antenna systems can be further categorized into MIMO systems, multiple-input single-output (MISO) systems, or single-input multiple-output (SIMO) systems. Further, if the antennas at the transmitter side are not co-located at a single device, such a system is referred to as a distributed-MIMO system, i.e., multiple distributed transmitters simultaneously communicate with a single multi-antenna receiver.

Synchronization challenge

In multi-antenna systems, multiple signal streams arrive at the receive antenna from different transmit antennas resulting in multiple timing offsets (MTOs). In some special cases, multiple timing offsets actually reduce to a single timing offset, e.g., if multiple antennas are co-located at a single transmitter device, then the transmit filters can be synchronized easily and the multiple signal streams arriving at the receive antenna experience approximately the same propagation delay.

If the transmit antennas are fed through independent oscillators, the received signal at the receive antenna is affected by multiple carrier frequency offsets (MCFOs) because of the existence of independent frequency offset between each transmit antenna oscillator and the receive antenna oscillator. On the other hand, if the transmit antennas are equipped with a single oscillator, the received signal at the receive antenna is affected by a single frequency offset. Thus, each receive antenna has to estimate and compensate for a single or multiple timing and frequency offsets, depending on the system model assumptions including Doppler fading.

In the case of distributed antenna systems, the receiver has to estimate and compensate for multiple CFOs and multiple TOs because each distributed transmit antenna is equipped with its own oscillator and multiple signal streams arriving at the receive antenna experience different propagation delays. Thus, in practice, the number of distributed antennas may need to be limited to avoid synchronization and pilot overhead associated with obtaining multiple CFOs and TOs.

Literature review

The summary of the research carried out to achieve timing and carrier synchronization in single-carrier multi-antenna communication systems is given in Table 8:

  1. 1.

    The estimation or compensation of CFO alone is studied in [160167].

  2. 2.

    The joint timing and carrier synchronization is studied in [168170].

The categorized papers differ in terms of channel model, channel estimation requirement, or pilot/training requirement. They also differ in terms of proposing estimation, compensation, joint channel estimation, or estimation lower bound. Further details or differences are provided in the last column of Table 4, which indicates if any additional parameter, e.g., phase noise (PHN), IQ imbalance, or direction of arrival (DoA) estimation, is considered or if space-time block coding (STBC), space frequency block coding (SFBC), or codebook design is considered.

Summary

Synchronization in single-carrier multi-antenna communication systems has not received as much attention compared to synchronization in multi-carrier multi-antenna communication systems. This may not be surprising since the latter is adopted in current wireless cellular standards. Still, single-carrier multi-antenna communication has got its importance in microwave backhaul links [171]. The important problem of joint estimation and compensation of MTOs and MCFOs has been considered in [170]. The authors assume pilot-free systems to propose blind synchronization. Generally, performance improvement is expected in the presence of training-based estimation and compensation. This, along with hardware implementation of the relevant algorithms, can be the subject of possible future extensions.

Multi-carrier multi-antenna communication systems

System model

In multi-carrier multi-antenna systems, the information at each antenna is modulated over multiple carriers. Thus, apart from the IFFT/CP addition and CP removal/FFT operations at each transmit and receive antennas, respectively, the system model for multi-carrier multi-antenna communication systems is similar to the one described for single-carrier multi-antenna systems presented in Section 3.1.1.

Synchronization challenge

Similar to single-carrier multi-antenna systems, the signal arriving at the receive antenna can potentially be affected by multiple TOs and multiple CFOs, when the transmit antennas are fed by different oscillators and are distant from one another. Due to multiple carriers, the presence of multiple TOs and multiple CFOs results in strong ISI and ICI. The synchronization challenge is to jointly estimate and compensate for the effect of multiple TOs and multiple CFOs in order to mitigate ISI and ICI and decode the signal from multiple antenna streams.

Literature review

The summary of the research carried out to achieve timing and carrier synchronization in single-carrier multi-antenna communication systems is given in Table 9:

  1. 1.

    The estimation or compensation of TO and CFO alone is studied in [172174] and [175198], respectively.

  2. 2.

    The joint timing and carrier synchronization is studied in [199203].

The number of oscillators considered by different papers at the transmitter and receiver, respectively, are given under the “Tx/Rx Oscillator” column. The categorized papers differ in terms of channel model, channel estimation requirement, or pilot/training requirement. They also differ in proposing estimation, compensation, joint channel estimation, or estimation lower bound. Further details or differences are provided in the last column of Table 9, which indicates if additional parameters, e.g., phase noise or IQ imbalance, are considered or if STBC, SFBC, coding, or hardware implementation is considered.

Summary

Compared to the estimation of single TO and single CFO, estimation of MTOs and MCFOs is more challenging, due to pilot design issues, overhead, pilot contamination problem, complexity, and non-convex nature of optimization problems. Considering multiple oscillators at the transceiver, joint estimation of MTOs and MCFOs has been considered in [200, 203] only. Particularly, the authors in [203] propose a compact TS design. However, both papers, [200, 203], do not consider channel estimation as it may help in improving the estimation performance of synchronization impairments, and further, they do not suggest algorithms for compensation of MTOs and MCFOs. Though joint estimation and compensation of timing and carrier frequency offset has been studied, e.g., in [199, 202], however, their system model consider single oscillator at the transceiver. The shortcomings in the above key papers can be the subject of possible future research.

Cooperative communication systems

In cooperative communication systems, the information transmission between the two communicating nodes is accomplished with the help of an intermediate relay. Let us assume a general scenario with the presence of multiple relays. There are two important types of cooperative communication networks:

  • One-way relaying network (OWRN), where information transmission occurs in one direction via intermediate relays.

  • Two-way relaying network (TWRN), where information transmission occurs simultaneously in both directions and both nodes exchange their information with the help of intermediate relays.

The relays themselves can operate in different modes. The two most common modes are (i) decode-and-forward (DF) and (ii) amplify-and-forward (AF) operation. In DF mode, the relays decode the received signal and forward the decoded signal to the intended destination node(s). In AF mode, the relays do not decode the received message and simply amplify and forward the received signal.

In the following subsections, we review the recent literature that deals with timing and carrier synchronization in single-carrier and multi-carrier cooperative communication systems.

Single-carrier cooperative communication systems

Since the relaying operations are different in DF and AF cooperative communication systems, so too are their synchronization methodologies. In AF, for example, the relays may not be required to convert the received passband signal to baseband and to perform carrier synchronization [20]. Generally, the synchronization parameters to be estimated differ in OWRNs and TWRNs. In TWRN, there is self-interference, which affects the way how the synchronization problem is formulated. In the following subsections, we provide separate literature reviews for synchronization in AF-OWRN, AF-TWRN, DF-OWRN, and DF-TWRN for single-carrier communication systems.

Decode-and-forward one-way relaying network

System model

The communication generally takes place in two phases. During the first phase, the source transmits the information to the relays. During the second phase, the relays decode the received signal and forward it to the destination. Typically, it is assumed that the direct communication link between the source and the destination is absent or blocked due to some obstacles. However, in general, there could be a direct communication link between them. In such a case, the destination also hears the source message during the first phase and coherently combines it with the message received during the second phase.

Synchronization challenge

In DF-OWRN, during the first phase of the two-phase communication process, the synchronization between the source and the relays or between the source and the destination (in the presence of direct link) is achieved by estimating and compensating for a single TO and CFO between the source and each relay or between the source and the destination (in the presence of direct link). During the second phase, the synchronization between the relays and the destination is achieved by estimating and compensating for the multiple TOs and multiple CFOs between the multiple relays and the destination. Note that in the case of a single relay, only a single TO and a single CFO are required to be estimated and compensated for at the destination during the second communication phase. Increasing the number of relays raises the challenge of pilot design and estimation overhead.

Literature review

The summary of the research carried out to achieve timing and carrier synchronization in single-carrier DF-OWRN is given in Table 10:

  1. 1.

    Estimation or compensation of timing offsets alone and frequency offsets alone is studied in [204206] and [19, 207209], respectively.

  2. 2.

    Joint timing and carrier synchronization is studied in [20, 210213].

Further details or differences are provided in the last column of Table 10, which indicates whether STBC, SFBC, or training sequence (TS) design is considered.

Decode-and-forward two-way relaying network

System model

TWRNs allow for more bandwidth efficient use of the available spectrum since they allow for simultaneous information exchange between the two nodes. In TWRNs, it is usually assumed that there is no direct communication link between the two nodes. During the first phase of the two-phase communication process, the information arrives at the relays from the two nodes. The signals from the two nodes are superimposed at the relays. During the second phase, the relays decode the exclusive OR (XOR) of the bits from the received superimposed signal and then broadcast a signal constructed from the XOR of the bits back to the two nodes [214].

Synchronization challenge

The synchronization challenge during the first communication phase of DF-TWRN is unlike that for DF-OWRN. In DF-TWRN, the relays receive the superimposed signals from the two nodes during the first communication phase. Thus, unlike DF-OWRN, the received signal at each relay during the first communication phase is a function of two TOs and two CFOs, which need to be jointly estimated and compensated for in order to decode the modulo-2 sum of the bits from the two nodes. The synchronization challenge during the second communication phase of DF-TWRN is similar to that described for DF-OWRN in Section 4.1.1. Another challenge for TWRN is the pilot design in the presence of self-interference at the relay node.

Literature review

A summary of the research carried out to achieve timing and carrier synchronization in single-carrier DF-TWRN is given in Table 10. There is only one paper in the last 5 years that falls in this category and proposes joint estimation and compensation of timing offsets [214]. Most of the research has considered AF relaying for TWRN due to its implementation advantages.

Amplify-and-forward one-way relaying network

System model

The system model for AF-OWRN is similar to that of Section 4.1.1 for DF-OWRN. However, instead of DF operation, the relays amplify and forward the source information.

Synchronization challenge

The synchronization challenge for AF-OWRN is quite similar to that described for DF-OWRN in Section 4.1.1, except for one important difference. In DF-OWRN, the signal is decoded at the relay and the received signal at the destination is impaired by multiple TOs and multiple CFOs only between the relays and the destination. In AF-OWRN, the received signal at the destination is affected not only by the multiple TOs and multiple CFOs between the relays and the destination (as in the case of DF-OWRN) but also by the residual TOs and CFOs between the source and the relays. This is due to imperfect synchronization during the first communication phase, and the amplified and forwarded signal from the relays is a function of the residual TOs and CFOs between the source and the respective relays.2

Literature review

The summary of the research carried out to achieve timing and carrier synchronization in single-carrier AF-OWRN is given in Table 10:

  1. 1.

    Estimation or compensation of timing offset alone and frequency offset alone is studied in [206, 215, 216] and [19], respectively.

  2. 2.

    Joint timing and carrier synchronization is studied in [20, 217, 218].

Amplify-and-forward two-way relaying network

System model

During the first phase, similar to DF-TWRN, the relays receive the superimposed signal from the two nodes. During the second phase, the relays amplify and broadcast the superimposed signal back to the two nodes [214].

Synchronization challenge

In AF-TWRN, when the relays receive the superimposed signals from the two nodes during the first communication phase, each relay only needs to carry out timing synchronization, i.e., estimate and compensate for the TOs between the two nodes and the relay [219, 220]. The reason will be explained shortly. During the second communication phase, the relays amplify and broadcast the time-synchronized version of the superimposed signal. Each node then needs to estimate and compensate for the MTOs between the relays and itself and the sum of the multiple CFOs from the other node-to-relays-to-itself.

Note that each node in this case does not need to estimate and compensate for the multiple CFOs from itself to relays to the other node because the effect of CFOs between itself and the relays during the first communication phase is canceled by the effect of CFOs between the relays and itself during the second communication phase due to the use of the same oscillators [219, 220]. Due to this very reason, the authors in [219, 220] propose to only perform timing synchronization at the relay nodes during the first communication phase in AF-TWRN.

Literature review

The summary of the research carried out to achieve timing and carrier synchronization in single-carrier AF-OWRN is given in Table 10:

  1. 1.

    The estimation or compensation of timing offset alone and frequency offset alone is studied by [221223] and [219, 224], respectively.

  2. 2.

    The joint timing and carrier synchronization is studied by [220].

Summary

In single-carrier cooperative communication systems, few recent research works have considered the important problem of both estimation and compensation of MTOs and MCFOs in the presence of channel impairments [20, 210, 217, 220]. Particularly, synchronization in OWRNs is studied by [20, 210, 217], where [210] proposes blind synchronization with blind source separation and relay selection and [20, 217] proposes training-based synchronization. AA Nasir et al. [20] proposed ML-based computationally complex compensation algorithm, and the shortcoming was overcome in the follow-up work [217] where MMSE-based efficient compensation algorithm was developed. Finally, synchronization in TWRNs is studied by [220]. There are still many open research problems to solve in this area, e.g., training design in the presence of MTOs and MCFOs or hardware implementation of the proposed algorithms.

Multi-carrier cooperative communication systems

The following subsections review the literature for synchronization in AF-OWRN, AF-TWRN, DF-OWRN, and DF-TWRN for multi-carrier communication systems. Since most of the papers consider orthogonal frequency division multiplexing (OFDM) as a special case of multi-carrier communication system, the system model and synchronization challenge below are presented for OFDM systems.

Decode-and-forward one-way relaying network

System model

Apart from the IFFT/CP addition and CP removal/FFT operations at the transmitter and receiver side at each node, respectively, the system model for DF-OWRN for multi-carrier systems is similar to that described for single-carrier DF-OWRN presented in Section 4.1.1.

Synchronization challenge

The synchronization challenge during the first communication phase between the source and the relays is similar to that presented for SISO multi-carrier systems in Section 2.2.2. During the second communication phase, the relays decode the received signal and forward it to the destination. Thus, the resulting signal at the destination is affected by multiple TOs and multiple CFOs resulting in strong ISI and ICI. The synchronization challenge is to jointly estimate and compensate for the effect of multiple TOs and multiple CFOs in order to mitigate ISI and ICI.

Literature review

The summary of the research carried out to achieve timing and carrier synchronization in single-carrier DF-OWRN is given in Table 11:

  1. 1.

    Estimation or compensation of frequency offset alone is studied in [225237].

  2. 2.

    Joint timing and carrier synchronization is studied in [238241].

Further details or differences are provided in the last column of Table 11, which indicates if STBC or SFBC is considered.

Decode-and-forward two-way relaying network

System model

Other than the IFFT/CP addition and CP removal/FFT operations at the transmitter and receiver side at each node, respectively, the system model for DF-TWRN for multi-carrier systems is similar to that for single-carrier systems presented in Section 4.1.2.

Synchronization challenge

The received signal at each relay during the first communication phase is a function of two TOs and two CFOs, which need to be jointly estimated and compensated. Thus, the synchronization challenge is similar to that described for the second communication phase of DF-OWRN with two TOs and two CFOs. Moreover, the synchronization challenge during the second communication phase of DF-TWRN is also similar to the one described for the second communication phase of DF-OWRN in Section 4.2.1.

Literature review

To the best of our knowledge, no paper in the last 5 years falls into this category, since the research in the synchronization of multi-carrier TWRN has considered AF relaying.

Amplify-and-forward one-way relaying network

System model

The system model for AF-OWRN for multi-carrier systems is similar to the one for single-carrier systems presented in Section 4.1.3. However, being an OFDM system, there are IFFT/CP addition and CP removal/FFT operations at the source transmitter and the destination receiver, respectively. Note that unlike DF-OWRN for multicarrier systems, the FFT and IFFT operations are not usually conducted at the receiver and transmitter of the relays, respectively.

Synchronization challenge

The synchronization challenge during the first communication phase for AF-OWRN is similar to the one described for DF-OWRN in Section 4.2.1. During the second communication phase, similar to AF-OWRN in single-carrier systems, the received signal at the destination is affected by not only the multiple TOs and multiple CFOs between the relays and the destination, but also by the residual TOs and CFOs between the source and the relays, which can cause additional ISI and ICI. The effects of ISI and ICI are required to be mitigated to achieve synchronization.

Literature review

The summary of the research carried out to achieve timing and carrier synchronization in single-carrier AF-OWRN is given in Table 11. The estimation or compensation of timing offset alone and frequency offset alone is studied in [225, 242246] and [247], respectively. Further details or differences are provided in the last column of Table 11, which indicates if any additional parameter, e.g., phase noise (PHN) estimation, is considered or if STBC or any other type of space coding is considered.

Amplify-and-forward two-way relaying network

System model

Apart from the IFFT/CP addition and CP removal/FFT operations at the transmitter and receiver, respectively, the system model for AF-TWRN for multi-carrier systems is similar to the one for single-carrier systems presented in Section 4.1.4. Note that unlike DF-TWRN for multicarrier systems, the FFT and IFFT operations are not usually conducted at the receiver and transmitter of the relays, respectively.

Synchronization challenge

The required parameters to be estimated and compensated to achieve synchronization in multi-carrier AF-TWRN are similar to the one described in the synchronization challenge of single-carrier AF-TWRN in Section 4.1.4. The difference is to mitigate the effect of ISI and ICI in OFDM systems.

Literature review

The summary of the research carried out to achieve timing and carrier synchronization in single-carrier AF-OWRN is given in Table 11. The estimation or compensation of frequency offset alone is studied in [248250]. The categorized papers differ in the sense that training sequence design is proposed in [248], joint CFO estimation and compensation with channel estimation is studied in [249], and CFO estimation alone is proposed in [250].

Summary

In multi-carrier cooperative communication systems, the problem of joint estimation and compensation of MTOs and MCFOs has been considered in very few works, e.g., [228]. Future research investigations may help to achieve efficient algorithms. In addition, solution to the particular problem in TWRNs and the derivation of CRLBs are open research areas in this field. Moreover, most of the solutions are pilot based. Hence, it is a challenging open problem to design semiblind and blind estimators.

Multiuser/multicell interference networks

SC-FDMA uplink communication systems

Single-carrier frequency division multiple access (SC-FDMA) is an extension of SC-FDE to accommodate multiple users. In SC-FDMA uplink communication systems, multiple users communicate with a single receiver and the effect of channel distortion is equalized in frequency domain at the receiver. Like the SC-FDE receiver, FFT and IFFT modules are present in the SC-FDMA receiver. However, the SC-FDMA transmitter also incorporates FFT and IFFT modules. Disjoint sets of M subcarriers are assigned to each of the K users, and data symbols from each user are modulated over a unique set of subcarriers through an M-point FFT operation. Next, KM-point IFFT is applied to transform the signal to time domain, such that the output of FFT is applied to the user-specified M inputs of IFFT block and 0 is applied to the remaining (K−1)M inputs of IFFT block. Next, cyclic prefix is appended at the start of the transmission block to mitigate the multipath channel effect. At the receiver side, following KM-point FFT and frequency domain equalization, K of M-point IFFT operations are applied to decode the information from the K users. SC-FDMA can also be interpreted as a linearly precoded OFDMA scheme, in the sense that it has an additional FFT processing step preceding the conventional OFDMA processing.

Due to the presence of an independent oscillator at each transmitting user and due to different propagation delays between each user and the receiver, the received signal in the SC-FDMA uplink communication system suffers from multiple CFOs and multiple TOs. The combined effect of TOs and CFOs results in both ISI and loss of orthogonality among subcarriers, which, in turn, generates ICI and multiple access interference (MAI) at the receiver. Thus, in order to decode information from each user, a receiver has to estimate multiple TOs and multiple CFOs to compensate the effect of ICI, ISI, and MAI. In uplink communication systems, there is another synchronization challenge that the correction of one user’s frequency and timing at the receiver can misalign those of the other users [251].

It must be noted that LTE systems [252] use SC-FDMA for communication in the uplink. Synchronization is achieved through periodically transmitted primary and secondary synchronization signals from the base station. Any user who has not yet acquired the uplink synchronization can use the primary and secondary synchronization signals (transmitted by the base station) to first achieve synchronization in the downlink. Next, compensation for the propagation loss is made as part of the uplink random access procedure [252].

The research carried out to achieve timing and carrier synchronization in SC-FDMA uplink communication systems is summarized in Table 12:

  1. 1.

    The estimation or compensation of multiple CFOs alone is studied in [251, 253261]. Though [253255, 258261] consider the same channel model with known CSI in order to propose multiple CFO compensation, they differ in the following characteristics. Different channel allocation strategies and their effects on interference due to CFO is studied in [253]. An interference self-cancelation scheme to compensate for the effect of CFO is proposed by [254].

  2. 2.

    Joint timing and carrier synchronization is studied in [262]. Further, [255, 260] consider multiple antennas at the users and receiver, [260] proposes blind beamforming assuming that the multipath delay is greater than the cyclic prefix length, and [261] proposes MMSE-FDE. Finally, joint timing and carrier synchronization in SC-FDMA systems is proposed in [262].

Considering pilot-free transmission, joint estimation and compensation of timing and carrier frequency offsets is studied in [262]. The work can be extended to consider pilot-based systems in order to improve the estimation accuracy. Further, CRLB derivation in the presence of synchronization impairments and hardware implementation can also be the subject of future work.

OFDMA uplink communication systems

An OFDMA communication system is an extension of an OFDM system to accommodate multiple users. In OFDMA uplink communication systems, multiple users communicate with a single receiver. Unlike OFDM systems, where information of a single user is modulated over all subcarriers, in OFDMA uplink transmitter, each user transmits over a set of assigned subcarriers. For each user, a group of M modulated symbols is applied to the user-specified M inputs of IFFT block and 0 is applied to the remaining (K−1)M inputs of IFFT block. In the end, cyclic prefix is appended and information is transmitted from every user. At the receiver side, following cyclic prefix removal and FFT operation, equalization is carried out to decode the information from the K users.

Similar to SC-FDMA uplink communication system, the received signal in SC-FDMA uplink suffers from multiple CFOs and multiple TOs. The combined effects of TOs and CFOs results in ISI, ICI, and MAI. The receiver has to estimate and compensate the effect of multiple TOs and multiple CFOs in order to decode information from each user. Similar to SC-FDMA uplink communication systems, the correction of one user’s frequency and timing at the receiver can misalign the other users.

The research to achieve timing and carrier synchronization in OFDMA uplink communication systems is summarized in Table 13. Almost all categorized papers study carrier synchronization only, [263290], except the authors in [291] consider ranging scheme to propose timing estimation in OFDMA uplink communication system. In addition, the authors in [292] provide solutions for synchronizing both the timing and frequency errors of multiple unsynchronized users in OFDMA-based spectrum sharing system. Further extension may consider the derivation of CRLBs in the presence of impairments and hardware implementation design. Overall, though the tabulated papers consider the same channel model, they differ with respect to providing joint channel estimation, requiring pilot/training, or providing lower bound. Further details or differences are provided in the last column of Table 13.

Code division multiple access communication systems

Code division multiple access (CDMA) communication systems allow multiple transmitters to send information simultaneously to a single receiver. All users share the same frequency and time resources. To permit this to be achieved without undue interference, CDMA employs spread-spectrum technology, i.e., either direct-sequence spread-spectrum (DS-CDMA) or frequency hopping spread spectrum (FH-CDMA):

  • In DS-CDMA, a unique (orthogonal or nearly orthogonal) spreading code is assigned to each transmitter. A modulated signal for each user is spread with a unique spreading code at the transmitter. Finally, at the receiver, information for each user is dispread by using the same unique despreading code. All users share the full available spectrum. Note that multi-carrier CDMA (MC-CDMA) spreads each user signal in the frequency domain, i.e., each user signal is carried over multiple parallel subcarriers and the spreading codes differ per subcarrier and per user.

  • FH-CDMA uses a short-term assignment of a frequency band to various signal sources. At each successive time slot of brief duration, the frequency band assignments are reordered. Each user employs a PN code, orthogonal (or nearly orthogonal) to all the other user codes, that dictates the frequency hopping band assignments.

Due to MUI, timing and carrier synchronization in CDMA is very challenging since compensating TO and CFO for one user may cause synchronization loss to other users. Particularly in CDMA systems, the presence of TOs and CFOs destroys the orthogonality of the spreading codes. The design of special spreading and despreading codes that are robust to synchronization errors is also a challenging task. Moreover, since the received signal powers from different users may vary significantly, the correct synchronized despreading may be challenging. Synchronization also faces multipath delay spread since PN chips have a short time duration. In MC-CDMA systems, due to the presence of CFOs, orthogonality among the subcarriers is lost and ICI is generated. Thus, the synchronization challenge is to estimate multiple TOs and multiple CFOs, compensate for MUI and ICI, and design proper spreading and despreading codes to decode information from each user.

Compared to DS-CDMA, the synchronization challenge is slightly reduced in FH-CDMA, because FH-CDMA synchronization has to be within a fraction of a hop time. Since spectral spreading does not use a very high hopping frequency but rather a large hop-set, the hop time will be much longer than the DS-CDMA system chip time. Thus, an FH-CDMA system allows for a larger synchronization error [293]. Moreover, in FH-CDMA, there is no need for synchronization among user groups, only between transmitter and receiver within a group is required.

Table 14 summarizes the research carried out to achieve timing and carrier synchronization in CDMA multiuser communication systems. All categorized papers consider DS spread spectrum technology and study the carrier synchronization alone [294301], where as identified in the second column of Table 14, [294, 295] consider single-carrier communication and [296301] consider multi-carrier communication. In addition, as detailed in Table 14, the categorized papers differ with respect to channel model, providing joint channel estimation, requiring pilot/training, proposing estimation or compensation of multiple CFO, or providing lower bound. Note that further details or differences are provided in the last column of Table 14.

The joint estimation and compensation of carrier frequency offsets has been considered in [298, 300]. Their work can be extended to include timing synchronization.

Cognitive radio-based communication systems

Cognitive radio networks allow unlicensed secondary users (SUs) access to the spectrum of the licensed primary users (PUs), without impairing the performance of the PUs. Depending on the spectrum access strategy, there are three main cognitive radio network paradigms [302]:

  • In the underlay cognitive networks, SUs can concurrently use the spectrum occupied by a PU by guaranteeing that the interference at the PU is below some acceptable threshold [303]. Thus, SUs must know the channel gains to the PUs and are also allowed to communicate with each other in order to sense how much interference is being created to the PUs.

  • In the overlay cognitive networks, there is tight interaction and active cooperation between the PUs and the SUs. Thus, SUs use sophisticated signal processing and coding to maintain or improve the PU transmissions while also obtaining some additional bandwidth for their own transmission.

  • In interweave cognitive networks, the SUs are not allowed to cause any interference to the PUs. Thus, SUs must periodically sense the environment to detect spectrum occupancy and transmit opportunistically only when the PUs are silent [302].

In the context of interweave cognitive networks, SUs sense the spectrum to detect the presence or absence of PUs and use the unoccupied bands while maintaining a predefined probability of missed detection. Different methods are used to detect the presence of PUs such as matched filtering, energy detection, cyclostationary detection, wavelet detection, and covariance detection. For multi-carrier OFDM systems, PUs adopt OFDM modulation and SUs make use of cyclic prefix, pilot tones of OFDM signals, or cyclostationarity to detect the PUs. In cognitive radio-based communication systems, the presence of timing and frequency offset affects the spectrum sensing performance and may result in false detection by the SUs. Accurate estimation and compensation of timing and frequency offsets from the PUs’ signal is necessary to lead to the correct decision about the spectrum availability. However, this is challenging given that SUs do not have access to pilot symbols and may need to estimate these parameters in a blind fashion. Blind synchronization algorithms are also known to be less accurate, which introduces new challenges to spectrum sensing in the presence of synchronization errors in cognitive radio networks.

Recent research in synchronization for cognitive radio-based communication systems is summarized in Table 15. The papers in Table 15 assume interweave cognitive networks, except for [304, 305], which consider underlay and both underlay/overlay-based cognitive networks. In Table 15,

  1. 1.

    The carrier synchronization alone is studied in [304311], where as identified in second column of Table 15, [306, 307] consider single-carrier communication and [304, 305, 308311] consider multi-carrier communication.

  2. 2.

    The joint timing and carrier synchronization is proposed in [312316], where as identified in second column of Table 15, [312] considers single-carrier communication and [313316] consider multi-carrier communication.

The number of primary users are listed under the “PUs” column. As detailed in Table 15, the categorized papers differ in channel model, providing joint channel estimation, requiring CSI or training, proposing estimation or compensation, or providing lower bound. If applicable, further details for some paper, e.g,. training sequence design, STBC, MIMO, or hardware design consideration, are provided in the last column of Table 15.

The important problem of joint estimation and compensation of timing and carrier frequency offsets has been considered in [313, 316] for training-based and pilot-free communication systems. The future work in this area may build upon them to develop more efficient estimation and compensation techniques and hardware implementation.

Distributed multiuser communication systems

In distributed multiuser communication systems, multiple distributed users try to communicate with a common receiver. Cooperation may exist among the distributed users to transmit the same information, i.e., broadcasting. On the other hand, each user might send its own data, which can cause MUI at the receiver. The receiver can employ successive interference cancelation to decode information from the desired user. Both single-carrier and multi-carrier modulation schemes can be employed by the multiple users. Due to the presence of independent oscillator at each transmitting user, the Doppler effect, and the existence of a different propagation delay between each user and the receiver, the received signal may suffer from multiple CFOs and multiple TOs. The receiver has to jointly estimate and compensate the effect of these synchronization impairments in order to decode the desired information. Note that users in distributed multiuser communication systems may cooperate among themselves, which may not be possible in cooperative relaying networks due to the latter’s simpler assumed relay topology.

Table 16 summarizes the research carried out to achieve timing and carrier synchronization in distributed multiuser communication systems. All listed papers [317321] consider multi-carrier communication. Carrier synchronization alone is studied by [317]. Joint timing and carrier synchronization is proposed in [318321], where joint estimation and compensation design is considered by [319, 321] and only compensation of timing and carrier frequency offsets is studied in [318, 320]. Different from [318, 320], [319, 321] also considers joint channel estimation. The important problem of joint estimation and compensation of MCFOs and MTOs is studied in [319, 321] considering TD-LTE cell search and MU-MIMO downlink communication systems. These research works can be treated as baseline to further extend for the algorithm design in the presence of phase noise, I/Q imbalance, or other impairments.

Coordinated multipoint transmission/reception-based communication systems

In coordinated multipoint transmission/reception (CoMP) communication systems, geographically separated base stations (also referred to as transmission points) coordinate and jointly process the signal transmission to multiple users at the cell edges. Thus, CoMP is also referred to as multicell cooperation. CoMP techniques can be broadly classified into (i) coordinated scheduling and coordinated beamforming (CS/CB), (ii) joint transmission (JT), and (iii) transmission point selection (TPS) [322]. Note that the transmission from the base stations can take place over single or multiple carriers. Some main points regarding the CoMP techniques are as follows:

  • In coordinated scheduling and coordinated beamforming, multiple coordinated transmission points only share the CSI for multiple users. The data packets that need to be conveyed to the users are available only at the respective transmission point to which each user belongs. Thus, every base station coordinates with its cell edge user through beamforming. The users may experience residual interference from the other cells’ communication depending on the location of the cell edge users.

  • In joint transmission, multiple coordinated transmission points share both CSI and the data packets to be conveyed to all users. Thus, the same data is simultaneously transmitted to the intended user from multiple coordinated transmission points with appropriate beamforming weights.

  • Transmission point selection can be regarded as a special form of JT, where transmission of beamformed data for a given user is performed at a single transmission point at each time instance. In addition, both CSI and the data are assumed to be available at multiple coordinated transmission points. Thus, an appropriate transmission point with access to the best channel conditions for individual users can be scheduled. While one transmission point coordinates with the scheduled user, other transmission points may possibly communicate in parallel to their respective users.

The synchronization between coordinating base stations and the users can be achieved either in uplink or downlink transmission. For downlink CoMP, due to the different oscillators at the base stations and the different propagation delays between each base station and the user, the received signal at the user suffers from multiple CFOs and multiple TOs. The receiver has to estimate these parameters jointly and compensate for their effects in order to establish successful CB, JT, or TPS scheme among coordinating base stations. For uplink CoMP, multiple users communicate with the base stations, causing multiple CFOs and multiple TOs at the base station. The base stations have to compensate for the effect of these multiple synchronization parameters in order to synchronize data transmission to the users during downlink communication by adopting any CoMP scheme. Since CoMP uses the backhaul link for coordination among the base stations, synchronization parameters can be exchanged in order to enhance the synchronization performance.

Considering synchronization in CoMP-based communication systems, Table 17 summarizes the recent research:

  1. 1.

    The timing synchronization alone is studied by [323325], where as identified in the second column of Table 17, [323] considers single-carrier communication and [324, 325] consider multi-carrier communication.

  2. 2.

    Carrier synchronization alone is studied by [326331], where as identified in the second column of Table 17, [326] considers single-carrier communication and [327331] consider multi-carrier communication.

  3. 3.

    Joint timing and carrier synchronization considering single- and multi-carrier communication is analyzed in [332] and [333], respectively.

Considering the TPS scheme, joint estimation and compensation design of timing and carrier frequency offsets is studied in [333]. The modification of the proposed algorithm can be studied to achieve joint estimation and compensation of timing and carrier frequency offsets for CB and JT schemes in COMP systems.

The type of CoMP communication, CB, JT, or TPS, is indicated under the “Type” field in Table 17, where a blank field indicates that either only the training/estimation phase or uplink communication is considered by the authors. As presented in Table 17, the categorized papers differ based on assuming varying channel models, considering channel estimation, assuming the need for CSI or training, proposing either estimation or detection algorithms in the presence of synchronization errors, or driving lower bounds on estimation of TO and CFO. Further details about the assumed channel models in each paper are provided in the last column of Table 17.

Multicell-interference-based communication systems

In a multicell interference communication network, the received signal is contaminated by intercell interference. The interference can arise from the neighboring cells, when there is a universal frequency reuse, i.e., available frequency resources are reused in each cell in the network. This can cause interference among different tiers in a heterogeneous network, e.g., femtocell base stations may receive interfering signals from the macrocell user in addition to the desired signal from the femtocell user. The synchronization challenge for the receiver is to achieve timing and frequency synchronization with the desired signal in the presence of this interference. In addition to estimating and compensating TO and CFO between desired user and receiver, it also has to suppress intercell interference in order to decode the desired signal. To date, most approaches to synchronization have modeled the interference as an additive term that can be combined with the noise. However, this approach may be suboptimum for synchronization in heterogeneous networks.

The summary of the research carried out to achieve timing and carrier synchronization in multicell-interference-based communication systems is given in Table 18:

  1. 1.

    Timing synchronization alone considering single-carrier communication is analyzed in [334].

  2. 2.

    Carrier synchronization alone is studied in [335339], where [335] considers single-carrier communication and [336339] consider multi-carrier communication.

  3. 3.

    Joint timing and carrier synchronization considering multi-carrier communication is analyzed in [340, 341].

The information about the physical layer used, e.g., SC-FDE downlink, SC-FDMA uplink, or OFDMA/OFDM downlink is provided under “Physical Layer” field in Table 18. Finally, as detailed in Table 18, the categorized papers may also differ with respect to providing joint channel estimation, requiring CSI or training, proposing estimation or compensation, or providing lower bounds on estimation of synchronization parameters. Further details about the consideration of TS design, cell search, or DoA estimation is provided in the last column of Table 18. The joint estimation and compensation of timing and carrier frequency offsets in interference-limited communication systems is an open research problem.

UWB and non-CDMA-based spread spectrum communication systems

UWB communication systems

System model

UWB refers to a radio communication technique based on transmitting very short duration pulses, typically of nanoseconds or less, whereby the occupied bandwidth is very large. The technology is used at a very low energy level for short-range, high-bandwidth communication using a large portion of the radio spectrum (>500 MHz). UWB communication transmits in a manner that results in little to no interference to narrow band signals that may be operating in the same frequency band. The UWB technology allows communication either over baseband or RF. The baseband or carrierless communication system, known as impulse radio (IR) UWB communication system, employs time-hopping. The RF communication system mainly involves multi-carrier communication, referred to as multi-band (MB)-OFDM UWB communication system, and usually employs frequency-hopping. The brief system model detailing IR-UWB and MB-OFDM UWB communication systems are summarized as follows:

IR-UWB

In IR-UWB communications, a single data symbol is associated with several consecutive pulses, each located in its own frame. Accordingly, each data symbol is spread by sub-nanosecond pulses. Spreading of these pulses is achieved by time-hopping these low-duty cycle pulses and data modulation is accomplished by additional pulse position modulation. Pulse width indicates the center frequency of the UWB signal. As IR-based UWB does not use any carrier signal, it is also known as baseband, or carrierless or zero-carrier technology.

In a multipath environment, a fine resolution of multipath arrivals occurs due to large transmission bandwidth. This leads to reduced fading for each path because the transmitted data is in the form of pulses and significant overlap is prevented. Thus, to reduce the possibility of destructive combining [342]. Rake receivers [343] are employed to collect the signal energy of the multipath components, achieving much higher processing gain. Due to its significant bandwidth, an IR-based multiple-access system may accommodate many users, even in multipath environments. Multiple access to the channel is made possible by changing the pulse position within a frame according to a user-specific time-hopping code.

MB-OFDM UWB

Multiband OFDM (MB-OFDM) approach for UWB is based on multiple OFDM bands each with at least 500-MHz bandwidth and each OFDM band comprising multiple sub-carriers. It can also be thought of as a combination of frequency hopping with the sub-carriers occupying one band at one time and hopping according to a pre-defined hopping pattern.

Synchronization challenge

The synchronization challenge of the two different UWB technologies, IR-UWB and MB-OFDM-UWB, is given in the following two subsections.

IR-UWB

Timing errors as small as a fraction of a nanosecond can seriously degrade the system performance. Timing recovery can be viewed as a two-part process. The first part consists of estimating the beginning of the individual frames relative to the receiver’s clock ticks. This is called frame timing. The second part consists of identifying the first symbol of each frame in the incoming frame stream and is referred to as symbol timing.

Frequency offset in IR-UWB communication system arises due to the clocks at the transmitter and receiver that run independently at slightly different frequencies, albeit close to a common nominal value. Deviations from the nominal value are referred to as clock frequency offsets. Moreover, Doppler fading also plays a key role in causing frequency offset in IR-UWB communication systems.

MB-OFDM UWB

The MB-OFDM systems have the following distinctive characteristics compared to conventional OFDM systems: (1) different channel responses and channel energies across different bands, (2) different carrier frequency offsets across different bands, and (3) the interplay between timing and frequency hopping (a mismatched timing point at the receiver will yield mismatched frequency hopping and hence a significant performance degradation). These characteristics provide diversity but also additional design constraints, and hence, should be taken into account in the designs of synchronization, channel estimation, and equalization. Further, MB-OFDM-based UWB receivers are quite sensitive to carrier frequency estimation errors [344].

Literature review

The summary of the research carried out to achieve timing and carrier synchronization in UWB communication systems is given in Table 19:

  1. 1.

    Timing synchronization alone considering IR-UWB communication system is studied in [345, 346].

  2. 2.

    Carrier synchronization alone is studied in [347355], where as identified in second column of Table 19, [347349] consider IR-UWB communication and [350355] consider MB-OFDM UWB communication.

  3. 3.

    Joint timing and carrier synchronization considering IR-UWB and MB-OFDM UWB communication is proposed in [356] and [357, 358], respectively.

As detailed in Table 19, the categorized papers differ in respects of providing joint channel estimation, requiring CSI or training, proposing estimation or compensation, or providing lower bounds. If applicable, further details, e.g., consideration of MAI, TWR protocols, sampling frequency offset (SFO) estimation, or hardware implementations, are provided in the last column of Table 19. Note that when considering the TWR protocol, range estimation is composed of both time of arrival (TOA) estimation of a direct path as well as round trip time (RTT) estimation.

Summary

Considering MB-OFDM UWB systems, the problem of joint estimation and compensation of timing and carrier frequency offsets is studied in [357, 358], where the authors in [358] considered hardware implementation, too. Their work can be considered as a baseline research for further extension, e.g., algorithm design in the presence of phase noise, I/Q imbalance, or other impairments. Furthermore, joint estimation and compensation of timing and carrier frequency offsets in IR UWB systems can be the subject of future research.

Non-CDMA-based spread spectrum communication systems

In this section, the literature survey focuses on spread spectrum communication systems aside from CDMA. Note that the list of papers dealing with synchronization in CDMA systems is already provided in Section 5.3.

Spread spectrum techniques can be applied to overcome a jamming situation, i.e., when an adversary intends to disrupt the communication. There, the aim of spread spectrum communication is to make the transmitted signal such that it should be difficult to detect by an adversary, i.e., the signal should have a low probability of interception and should be difficult to jam. The term spread spectrum stems from the fact that the transmitted signal occupies a much wider frequency band than what is necessary. This enables the transmitter to hide its signal in a larger bandwidth. CDMA is realized by spread spectrum techniques, but security aspects and anti-jamming/anti-noise properties of SS communication may have become less important to CDMA communications since it has been adopted as a mobile standard.

Different spread spectrum techniques that use time-domain, frequency-domain, direct pseudonoise chip sequence, and multi-carrier have been proposed. Among the above techniques, frequency-domain spread spectrum employ multi-carrier communication. Similar to CDMA communication systems, non-CDMA-based spread-spectrum-based communication is also prone to synchronization errors. Thus, the synchronization challenge of non-CDMA-based spread spectrum communication is to estimate and compensate the effect of TO and CFO from the received signal before despreading it. Non-CDMA-based spread spectrum systems may feature their specific synchronization methods, e.g., a specific frequency synchronization technique that can be applied only to non-CDMA-based frequency-domain spread spectrum systems is presented in [359].

Table 20 summarizes the research carried out to achieve synchronization in non-CDMA-based spread spectrum communication systems:

  1. 1.

    The timing synchronization alone considering single-carrier communication is studied in [360].

  2. 2.

    Carrier synchronization alone considering single- and multi-carrier communication is proposed in [361] and [362], respectively.

  3. 3.

    Joint timing and carrier synchronization considering single and multi-carrier communication is proposed in [359] and [363], respectively.

As detailed in Table 20, the categorized papers differ with respect to proposing an estimation or compensation design. If applicable, further details whether Gabor division spread spectrum system (GD-S3) or frequency division spread spectrum system (FD-S3) is considered are provided in last column of Table 20. Considering single-carrier communication, the joint estimation of timing and carrier frequency offsets is studied in [359]. The work can be extended to consider the compensation of synchronization impairments. On the other hand, considering multi-carrier communication, the joint compensation of timing and carrier frequency offsets is studied in [363] and their work can be extended to consider joint estimation of synchronization impairments.

Future directions

With the further adoption of wireless technologies in new and emerging fields, it is anticipated that cellular systems may need to operate in a more distributed fashion. Moreover, to support a growing number of devices, cellular systems are expected to shrink their sizes to enable dense small-cell networks, employ multi-antenna cooperative relaying, and take advantage of new frequency bands and technologies. For example, recent articles shed light on the synchronization problem of a dense small-cell network [364] and relay-aided cooperative MIMO networks [365]. In this section, we discuss some promising directions for future research.

Millimeter-wave and terahertz frequencies: The scarcity of bandwidth has encouraged new research in the field of millimeter-wave and terahertz communications. Unlike the microwave band, there is a very large amount of underutilized bandwidth at these frequencies. However, millimeter-wave (30 to 300 GHz) and terahertz (300 to 1 THz) communication systems are affected by significant signal attenuation due to path loss and shadowing [366]. Moreover, oscillators at such high-carrier frequencies are not as accurate compared to their counterparts at microwave frequencies and, as such, millimeter-wave and terahertz are significantly more impacted by CFO and phase noise [366]. In addition to all of the above, amplifiers at millimeter-wave and terahertz are expected to operate in the saturation region to generate enough power to overcome the shadowing and path loss issues in these bands. As such, the received signal at these frequencies may be severely affected by nonlinearities [367].

As illustrated in Table 21, the research in synchronization for millimeter-wave (mmwave) systems is at an early stage. The first two papers in the list [368, 369] consider single-carrier MIMO link and devise frequency offset compensation [368] and initial cell search [369] with hardware level testing. The other papers in the list [370, 371] consider multi-carrier communication and propose joint channel estimation with CFO estimation and compensation design. In particular, [370] considers MIMO system with the presence of PHN, while [371] also proposes sampling frequency offset estimation and considers hardware implementation.

To highlight the above discussion and recent research activities, new synchronization algorithms for mmwave communication are needed that can operate at very low SNR, can still achieve end-to-end synchronization in the presence of severe amplifier nonlinearity, and can track impairments such as carrier frequency offset and timing offset jointly in the presence of significant phase noise.

Massive MIMO: One approach to achieve higher bandwidth efficiency and to support more users per base station is to use a very large number of antennas at the base station. Theoretical studies and measurement campaigns have shown that massive MIMO systems can overcome significant interference and the additive noise in cellular networks [372]. However, massive MIMO systems require the estimation of a very large number of channel and synchronization parameters at the cost of significant overhead. Due to the large number of antennas and users in a massive MIMO system, it is not possible to allocate distinct and orthogonal training sequences to each user. This creates pilot overlap, so massive MIMO systems are impacted by pilot contamination.

New algorithms are, therefore, needed to overcome pilot contamination, complexity, and overhead associated with synchronization in massive MIMO systems. The main approach to date has been on using time division duplex (TDD) and the resulting channel reciprocity [372]. However, it is expected that as communication systems migrate to mmwave and terahertz frequencies, frequency division duplex (FDD) systems may be deployed more often than TDD systems. In addition, most of the synchronization work reported to date been based on the assumption that the massive MIMO channel is full rank, which is contrary to practical measurement results. In fact, massive MIMO channels are expected to be severely sparse [373], and channel sparsity ought to be used to reduce synchronization overhead.

Full-duplex communications: Although the next-generation cellular networks are expected to migrate to the mmwave and terahertz bands, the microwave band will continue to play an important role in 5G and beyond due to its favorable propagation characteristics. Hence, to achieve better bandwidth efficiency at microwave frequencies, full-duplex communication systems are considered for broad adoption in next-generation cellular systems [374]. Although significant research has been carried out [375, 376], the issue of synchronization in full-duplex communication systems continues to be an open area of research. This can be attributed to the challenges associated with estimating synchronization parameters in the presence of severe self-interference encountered in full-duplex communication systems. One solution could be to leverage the advancements in the field of antenna design and use reconfigurable and directional antennas at the transceiver to reduce self-interference. This combined with algorithms that can blindly track the synchronization parameters in the presence of self-interference may advance the research in this field.

Radio frequency energy harvesting communication systems: Radio frequency (RF)-enabled simultaneous information and power transfer (SWIPT) has emerged as an attractive solution to power nodes in future wireless networks [377379]. The design of synchronization schemes for SWIPT has not yet been considered in the literature. This is a challenging task since information and power transfer may have fundamentally different objectives, e.g., interference degrades the quality of the information transfer but can actually be beneficial from the viewpoint of RF energy harvesting. The speed and reliability of the information transfer obviously depends on accurate synchronization. One might (misleadingly) think that synchronization is not necessary for power transfer since it does not affect the process of RF-DC conversion, e.g., a time-misaligned sine wave can still be rectified. However, carrier mismatch means that a receiver is not able to lock onto the RF signal in the first place. Thus, accurate synchronization is therefore needed for efficient power transfer. In this regard, fundamental problems include how to determine the optimum energy harvesting, pilot training, and data transmission times, in order to achieve maximum throughput and high synchronization accuracy in wireless energy harvesting systems.

Conclusions

In this survey, we have provided a comprehensive classification of the timing and carrier synchronization research published in the literature in the last 5 years. Important contributions of this work include Tables 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, and 21, which summarize the system model assumptions and the state-of-the-art synchronization solutions and their limitations for different communication systems, such as SISO, MIMO, cooperative relaying, and multiuser/multicell interference networks. Our hope is that this survey paper would enable researchers to quickly immerse themselves in the current state-of-the-art in this field. We also conclude the discussion of each communication system by highlighting key papers and some future possible extensions in the field. In the end, further directions for future research are also outlined.

Endnotes

1 The system model and the synchronization challenge for other types of multi-carrier systems, e.g., filter bank multi-carrier (FBMC) systems, are not considered in this survey. Their details can be found in the papers identified in Section 2.2.3.

2 In a relay transceiver design, which is different from the conventional relay transceiver design described above, the authors have proposed to only establish timing synchronization at the relay and estimate and compensate for the sum of multiple CFOs from the source to relays to destination at the destination [20].

References

  1. 1

    K David, N Jefferies, Wireless visions: a look to the future by the fellows of the WWRF. IEEE Veh. Technol. Mag. 7(4), 26–36 (2012).

  2. 2

    SD Blostein, H Leib, Multiple antenna systems: their role and impact in future wireless access. IEEE Commun. Mag. 41(7), 94–101 (2003).

  3. 3

    D Gesbert, S Hanly, H Huang, S Shamai Shitz, O Simeone, W Yu, Multi-cell MIMO cooperative networks: a new look at interference. IEEE J. Selected Areas Commun. 28(9), 1380–1408 (2010).

  4. 4

    T Hwang, C Yang, G Wu, S Li, GY Li, OFDM and its wireless applications: a survey. IEEE Trans. Veh. Techniol. 58(4), 1673–1694 (2009).

  5. 5

    JN Laneman, DNC Tse, GW Wornell, Cooperative diversity in wireless networks: Efficient protocols and outage behavior. IEEE Trans. Inf. Theory. 50(12), 3062–3080 (2004).

  6. 6

    R Pabst, BH Walke, DC Schultz, P Herhold, H Yanikomeroglu, S Mukherjee, H Viswanathan, M Lott, W Zirwas, M Dohler, H Aghvami, DD Falconer, GP Fettweis, Relay-based deployment concepts for wireless and mobile broadband radio. IEEE Commun. Mag. 42(9), 80–89 (2004).

  7. 7

    Y Yang, H Hu, J Xu, G Mao, Relay technologies for WiMAX and LTE-advanced mobile systems. IEEE Commun. Mag. 47(10), 100–105 (2009).

  8. 8

    JG Andrews, S Buzzi, W Choi, SV Hanly, A Lozano, ACK Soong, JC Zhang, What will 5G be?IEEE J. Selected Areas Commun.32(6), 1065–1082 (2014).

  9. 9

    The METIS (Mobile and wireless communications Enablers for the Twenty-twenty Information Society) 2020 Project (2013). [Online]. Available: http://www.metis2020.com/.

  10. 10

    M Morelli, C-CJ Kuo, M-O Pun, Synchronization techniques for orthogonal frequency division multiple access (OFDMA): A tutorial review. Proc. IEEE. 95(7), 1394–1427 (2007).

  11. 11

    A Magee, Synchronization in next-generation mobile backhaul networks. IEEE Commun. Mag. 48(10), 110–116 (2010).

  12. 12

    O Tipmongkolsilp, S Zaghloul, A Jukan, The evolution of cellular backhaul technologies: current issues and future trends. IEEE Commun. Surv. Tutorials. 13(1), 97–113 (2011).

  13. 13

    D Bladsjo, M Hogan, S Ruffini, Synchronization aspects in LTE small cells. IEEE Commun. Mag. 51(9), 70–77 (2013).

  14. 14

    H Steendam, M Ghogho, M Luise, E Panayirci, E Serpedin, Synchronization in wireless communications. EURASIP J. Wireless Commun. Netw. 2009(568369), 1–3 (2009).

  15. 15

    S Barbarossa, G Scutari, Bio-inspired sensor network design. IEEE Signal Proc. Mag. 24:, 26–35 (2007).

  16. 16

    A Tyrrell, G Auer, C Bettstetter, Emergent slot synchronization in wireless networks. IEEE Trans. Mob. Comput. 9(5), 719–732 (2010).

  17. 17

    H Meyr, M Moeneclaey, SA Fechtel, Digital communication receivers, synchronization, channel estimation, and signal processing. (JG Proakis, ed.) (Wiley Series in Telecom and Signal Processing, 1998).

  18. 18

    JK Cavers, An analysis of pilot symbol assisted modulation for Rayleigh fading channels. IEEE Trans. Veh. Technol. 40(4), 686–693 (1991).

  19. 19

    H Mehrpouyan, S Blostein, Bounds and algorithms for multiple frequency offset estimation in cooperative networks. IEEE Trans. Wireless Commun. 10(4), 1300–1311 (2011).

  20. 20

    AA Nasir, H Mehrpouyan, SD Blostein, S Durrani, RA Kennedy, Timing and carrier synchronization with channel estimation in multi-relay cooperative networks. IEEE Trans. Signal Process. 60(2), 793–811 (2012).

  21. 21

    D Djenouri, M Bagaa, Synchronization protocols and implementation issues in wireless sensor networks. A review. IEEE Syst. J.10(2), 617–627 (2016). (accepted to appear).

  22. 22

    E Serpedin, QM Chaudhari, Synchronization in Wireless Sensor Networks: Parameter Estimation, Performnace Benchmarks and Protocols (Cambridge University Press, 2011).

  23. 23

    Y-C Wu, Q Chaudhari, E Serpedin, Clock synchronization of wireless sensor networks. IEEE Signal Proc. Mag. 28(1), 124–138 (2011).

  24. 24

    NM Freris, H Kowshik, PR Kumar, Fundamentals of large sensor networks: Connectivity, capacity, clocks, and computation. Proc. IEEE. 98(11), 1828–1846 (2010).

  25. 25

    O Simeone, U Spagnolini, Y Bar-Ness, S Strogatz, Distributed synchronization in wireless networks. IEEE Signal Process. Mag. 25(5), 81–97 (2008).

  26. 26

    G Fettweis, M Lohning, D Petrovic, M Windisch, P Zillmann, W Rave, in Proc. IEEE PIMRC, 4. Dirty RF: a new paradigm, (2005), pp. 2347–2355.

  27. 27

    AK Poddar, UL Rohde, AM Apte, How low can they go?: Oscillator phase noise model, theoretical, experimental validation, and phase noise measurements. IEEE Microw. Mag. 14(6), 50–72 (2013).

  28. 28

    T Schenk, RF Imperfections in High-rate Wireless Systems (Springer, 2008).

  29. 29

    Google scholar top publications - signal processing. https://scholar.google.com.au/citations?view_op=top_venues&hl=en&vq=eng_signalprocessing, accessed: 19-06-2015.

  30. 30

    X Wu, Y Song, C Zhao, X You, Progressive frequency offset compensation in turbo receivers. IEEE Trans. Wireless Commun. 10(2), 702–709 (2011).

  31. 31

    Y Yin, Y Yan, C Wei, S Yang, A low-power, low-cost GFSK demodulator with a robust frequency offset tolerance. 61(9), 696–700 (2014).

  32. 32

    AM Rabiei, NC Beaulieu, Frequency offset invariant multiple symbol differential detection of MPSK. IEEE Trans. Commun. 59(3), 652–657 (2011).

  33. 33

    S Colonnese, S Rinauro, G Panci, G Scarano, Gain-control-free blind carrier frequency offset acquisition for QAM constellations. IEEE Trans. Signal Process. 58(1), 349–361 (2010).

  34. 34

    L Bai, Q Yin, H Wang, Analysis of carrier frequency offset estimation with multiple pilot block sequences. IEEE Commun. Lett. 14(5), 456–458 (2010).

  35. 35

    S Colonnese, S Rinauro, G Scarano, Frequency offset estimation for unknown QAM constellations. IEEE Trans. Commun. 60(3), 637–642 (2012).

  36. 36

    X Man, Z Xi, K Gao, E Zhang, A novel code-aided carrier recovery algorithm for coded systems. IEEE Commun. Lett. 17(2), 405–408 (2013).

  37. 37

    P Pedrosa, R Dinis, F Nunes, Iterative frequency domain equalization and carrier synchronization for multi-resolution constellations. IEEE Trans. Broadcast. 56(4), 551–557 (2010).

  38. 38

    Y-C Pan, S-M Phoong, A time-domain joint estimation algorithm for CFO and I/Q imbalance in wideband direct-conversion receivers. IEEE Trans. Wireless Commun. 11(7), 2353–2361 (2012).

  39. 39

    J Kim, H-G Ryu, Inter-subcarrier interference compensation in the frequency-hopped single-carrier frequency division multiple access communication system. IET Commun. 4(12), 1443–1451 (2010).

  40. 40

    C Zhang, Z Xiao, B Gao, L Su, D Jin, Three-stage treatment of TX/RX IQ imbalance and channel with CFO for SC-FDE systems. IEEE Commun. Lett. 18(2), 297–300 (2014).

  41. 41

    F Gong, G Shang, Y Li, K Peng, Initial-estimation-based adaptive carrier recovery scheme for DVB-S2 system. IEEE Trans. Broadcast. 58(4), 654–659 (2012).

  42. 42

    B Ramakrishnan, Maximum-likelihood based lock detectors for M-PSK carrier phase tracking loops. Electron. Lett. 48(4), 242–244 (2012).

  43. 43

    AA Nasir, S Durrani, RA Kennedy, in Proc. IEEE ICASSP. Mixture kalman filtering for joint carrier recovery and channel estimation in time-selective rayleigh fading channels, (2011), pp. 3496–3499.

  44. 44

    M Elgenedy, A Elezabi, in Proc. IEEE ICC. Blind symbol rate estimation using autocorrelation and zero crossing detection, (2013), pp. 4750–4755.

  45. 45

    HY Yan, P Hua, LJ Qiang, in Proc. IEEE SPAWC. New joint algorithm of blind doppler parameters estimation for high-order QAM signals, (2011), pp. 11–15.

  46. 46

    C Yan, H Wang, J Kuang, N Wu, M Zheng, in Proc. IEEE VTC Spring. Design of data-aided SNR estimator robust to frequency offset for MPSK signals, (2010).

  47. 47

    J Bhatti, N Noels, M Moeneclaey, in Proc. IEEE PIMRC. Low-complexity frequency offset and phase noise estimation for burst-mode digital transmission, (2011), pp. 1662–1666.

  48. 48

    H Zhao, H Wang, N Wu, J Kuang, in Proc. IEEE VTC Spring. A message passing approach to joint channel estimation and decoding with carrier frequency offset in time selective rayleigh fading channel, (2013).

  49. 49

    D Popp, V Venkateswaran, in Proc. IEEE PIMRC. Energy efficient M2M signaling with enhanced gateway: Detection and offset compensation, (2013), pp. 1050–1055.

  50. 50

    H Wang, C Yan, J Kuang, N Wu, Z Fei, M Zheng, in Proc. IEEE VTC Spring. Design and analysis of data-aided coarse carrier frequency recovery in DVB-S2, (2010).

  51. 51

    Z Sahinoglu, Improving range accuracy of IEEE 802.15.4a radios in the presence of clock frequency offsets. IEEE Commun. Lett. 15(2), 244–246 (2011).

  52. 52

    X Man, H Zhai, J Yang, E Zhang, Improved code-aided symbol timing recovery with large estimation range for LDPC-coded systems. IEEE Communications Letters. 17(5), 1008–1011 (2013).

  53. 53

    N Wu, H Wang, J Kuang, C Yan, Performance analysis of code-aided symbol timing recovery on AWGN channels. IEEE Trans. Commun. 59(7), 1975–1984 (2011).

  54. 54

    T Obara, K Takeda, F Adachi, in Proc. IEEE VTC Spring. Joint frequency-domain equalization & spectrum combining for the reception of SC signals in the presence of timing offset, (2010).

  55. 55

    J Bao, M Zhao, J Zhong, Y Cai, in Proc. IEEE VTC Spring. Iterative timing recovery with turbo decoding at very low SNRs, (2012).

  56. 56

    A Peng, G Ou, M Shi, Frequency estimation of single tone signals with bit transition. IET Signal Process.8(9), 1025–1031 (2014).

  57. 57

    Y-T Lin, S-G Chen, A blind fine synchronization scheme for SC-FDE systems. IEEE Trans. Commun. 62(1), 293–301 (2014).

  58. 58

    C Shaw, M Rice, Optimum pilot sequences for data-aided synchronization. IEEE Trans. Commun. 61(6), 2546–2556 (2013).

  59. 59

    J-C Lin, H-Y Hsu, Timing-delay and frequency-offset estimations for initial synchronisation on time-varying rayleigh fading channels. IET Commun. 7(6), 562–576 (2013).

  60. 60

    D Oh, S Kim, S-H Yoon, J-W Chong, Two-dimensional ESPRIT-like shift-invariant TOA estimation algorithm using multi-band chirp signals robust to carrier frequency offset. IEEE Trans. Wireless Commun. 12(7), 3130–3139 (2013).

  61. 61

    H Huh, JV Krogmeier, Frame synchronization of coded modulations in time-varying channels via per-survivor processing. IEEE Trans. Commun. 59(10), 2665–2670 (2011).

  62. 62

    E Hosseini, E Perrins, The cramer-rao bound for training sequence design for burst-mode CPM. IEEE Trans. Commun. 61(6), 2396–2407 (2013).

  63. 63

    E Hosseini, E Perrins, Timing, carrier, and frame synchronization of burst-mode CPM. IEEE Trans. Commun. 61(12), 5125–5138 (2013).

  64. 64

    AA Nasir, S Durrani, RA Kennedy, Particle filters for joint timing and carrier estimation: Improved resampling guidelines and weighted bayesian cramer-rao bounds. IEEE Trans. Commun. 60(5), 1407–1419 (2012).

  65. 65

    O Dobre, M Oner, S Rajan, R Inkol, Cyclostationarity-based robust algorithms for QAM signal identification. IEEE Commun. Lett. 16(1), 12–15 (2012).

  66. 66

    A Punchihewa, Q Zhang, OA Dobre, C Spooner, S Rajan, R Inkol, On the cyclostationarity of OFDM and single carrier linearly digitally modulated signals in time dispersive channels: theoretical developments and application. IEEE Trans. Wireless Commun. 9(8), 2588–2599 (2010).

  67. 67

    D Inserra, AM Tonello, in Proc. IEEE VTC Fall. DoA estimation with compensation of hardware impairments, (2010).

  68. 68

    C-S Lin, J-C Lin, Physical-layer transceiving techniques on data-aided orthogonal frequency-division multiplexing towards seamless service on vehicular communications. IET Commun. 7(8), 721–730 (2013).

  69. 69

    WY Xu, Carrier frequency offset tracking for constant modulus signalling-based orthogonal frequency division multiplexing systems. IET Commun.6(11), 1555–1561 (2012).

  70. 70

    C-H Tseng, C-D Chung, Concatenated precoded OFDM for CFO effect mitigation. IEEE Trans. Veh. Technol. 62(6), 2618–2632 (2013).

  71. 71

    C-F Wu, M-T Shiue, C-K Wang, Joint carrier synchronization and equalization algorithm for packet-based OFDM systems over the multipath fading channel. IEEE Trans. Veh. Technol. 59(1), 248–260 (2010).

  72. 72

    G Lu, J Wang, C Zhang, Z Wang, Hard decision directed frequency tracking for OFDM on frequency selective channel. Tsinghua Sci. Technol. 17(2), 202–208 (2012).

  73. 73

    C-L Wang, Y-C Huang, Intercarrier interference cancelation using general phase rotated conjugate transmission for ofdm systems. IEEE Trans. Commun. 58(3), 812–819 (2010).

  74. 74

    Z Zhu, H Leung, X Huang, Challenges in reconfigurable radio transceivers and application of nonlinear signal processing for rf impairment mitigation. IEEE Circ. Syst. Mag. 13(1), 44–65 (2013).

  75. 75

    K Xu, Y Xu, D Zhang, W Ma, On max-SINR receivers for HMT systems over a doubly dispersive channel. IEEE Trans. Vehicular Technol. 62(5), 2381–2387 (2013).

  76. 76

    T Ketseoglou, An optimized iterative (turbo) receiver for OFDM systems with type-I hybrid ARQ: Clipping and cfo cases. IEEE Trans. Wireless Commun. 9(8), 2468–2477 (2010).

  77. 77

    V Lottici, R Reggiannini, M Carta, Pilot-aided carrier frequency estimation for filter-bank multicarrier wireless communications on doubly-selective channels. IEEE Trans. Signal Process. 58(5), 2783–2794 (2010).

  78. 78

    X Li, Q Han, J Ellinger, J Zhang, Z Wu, in Proc. IEEE ICC. General total inter-carrier interference cancelation for OFDM high speed aerial vehicle communication, (2013), pp. 4698–4702.

  79. 79

    J Zhang, X Mu, L Hanzo, in Proc. IEEE VTC Spring. Joint channel, carrier-frequency-offset and noise-variance estimation for OFDM systems based on expectation maximization, (2010).

  80. 80

    Z Gao, M-A Ingram, in Proc. IEEE VTC Fall. Self-cancelation of sample frequency offset in OFDM systems in the presence of carrier frequency offset, (2010).

  81. 81

    M Guillaud, F Kaltenberger, in Proc. IEEE WCNC. Towards practical channel reciprocity exploitation: Relative calibration in the presence of frequency offset, (2013), pp. 2525–2530.

  82. 82

    N Andgart, F Nordstrom, in Proc. IEEE VTC Fall. Frequency offset estimation with increased nyquist frequency, (2010).

  83. 83

    F Gregorio, J Cousseau, S Werner, R Wichman, T Riihonen, in Proc. IEEE WCNC. Sequential compensation of rf impairments in OFDM systems, (2010).

  84. 84

    T-L Liu, W-H Chung, H Zhang, C-H Chung, C-H Ho, S-Y Kuo, in Proc. IEEE VTC Fall. Optimal frequency offsets with doppler spreads in mobile OFDM system, (2012).

  85. 85

    X Cai, Y-C Wu, H Lin, K Yamashita, Estimation and compensation of CFO and I/Q imbalance in OFDM systems under timing ambiguity. IEEE Trans. Veh. Technol. 60(3), 1200–1205 (2011).

  86. 86

    Y Liu, Z Tan, H Wang, K-S Kwak, Joint estimation of channel impulse response and carrier frequency offset for OFDM systems. IEEE Trans. Veh. Technol. 60(9), 4645–4650 (2011).

  87. 87

    OH Salim, A Nasir, H Mehrpouyan, W Xiang, S Durrani, RA Kennedy, Channel, phase noise, and frequency offset in OFDM systems: Joint estimation, data detection, and hybrid cramer-rao lower bound. IEEE Trans. Commun. 62(9), 3311–3325 (2014).

  88. 88

    S Rahimi, B Champagne, Joint channel and frequency offset estimation for oversampled perfect reconstruction filter bank transceivers. IEEE Trans. Commun. 62(6), 2009–2021 (2014).

  89. 89

    M Morelli, M Moretti, Fine carrier and sampling frequency synchronization in OFDM systems. IEEE Trans. Wireless Commun. 9(4), 1514–1524 (2010).

  90. 90

    H-Y Liu, R Yen, Effective adaptive iteration algorithm for frequency tracking and channel estimation in OFDM systems. IEEE Trans. Vehicular Technol. 59(4), 2093–2097 (2010).

  91. 91

    A Ishaque, G Ascheid, in Proc. IEEE PIMRC. I/Q imbalance and CFO in OFDM/OQAM systems: Interference analysis and compensation, (2013), pp. 386–391.

  92. 92

    E-P Simon, L Ros, H Hijazi, M Ghogho, Joint carrier frequency offset and channel estimation for OFDM systems via the EM algorithm in the presence of very high mobility. IEEE Trans. Signal Process. 60(2), 754–765 (2012).

  93. 93

    H Nguyen-Le, T Le-Ngoc, Pilot-aided joint cfo and doubly-selective channel estimation for OFDM transmissions. IEEE Trans. Broadcast. 56(4), 514–522 (2010).

  94. 94

    Z Cvetkovic, V Tarokh, S Yoon, On frequency offset estimation for OFDM. IEEE Trans. Wireless Commun. 12(3), 1062–1072 (2013).

  95. 95

    R Carvajal, JC Aguero, BI Godoy, GC Goodwin, EM-based maximum-likelihood channel estimation in multicarrier systems with phase distortion. IEEE Trans. Veh. Technol. 62(1), 152–160 (2013).

  96. 96

    A Punchihewa, VK Bhargava, C Despins, Blind estimation of OFDM parameters in cognitive radio networks. IEEE Trans. Wireless Commun. 10(3), 733–738 (2011).

  97. 97

    A Al-Dweik, A Hazmi, S Younis, B Sharif, C Tsimenidis, Carrier frequency offset estimation for OFDM systems over mobile radio channels. IEEE Trans. Veh. Technol. 59(2), 974–979 (2010).

  98. 98

    S Lmai, A Bourre, C Laot, S Houcke, An efficient blind estimation of carrier frequency offset in OFDM systems. IEEE Trans. Vehicular Technol. 63(4), 1945–1950 (2014).

  99. 99

    J-H Oh, J-G Kim, J-T Lim, Blind carrier frequency offset estimation for OFDM systems with constant modulus constellations. IEEE Commun. Lett. 15(9), 971–973 (2011).

  100. 100

    W Xu, J Zhang, Y Liu, P Zhang, A comment on “a blind OFDM synchronization algorithm based on cyclic correlation”. IEEE Signal Process. Lett. 17(4), 411–412 (2010).

  101. 101

    X Li, E Like, Z Wu, M Temple, in Proc. IEEE ICC. Highly accurate blind carrier frequency offset estimator for mobile OFDM systems, (2010).

  102. 102

    A Al-Dweik, A Hazmi, S Younis, B Sharif, C Tsimenidis, Blind iterative frequency offset estimator for orthogonal frequency division multiplexing systems. IET Commun. 4(16), 2008–2019 (2010).

  103. 103

    H-G Jeon, K-S Kim, E Serpedin, An efficient blind deterministic frequency offset estimator for OFDM systems. IEEE Trans. Commun. 59(4), 1133–1141 (2011).

  104. 104

    K Cai, X Li, J Du, Y. -C Wu, F Gao, CFO estimation in OFDM systems under timing and channel length uncertainties with model averaging. IEEE Trans. Wireless Commun. 9(3), 970–974 (2010).

  105. 105

    E-S Shim, J Kim, Y-H You, Low-cost integer frequency offset estimation for OFDM-based DRM receiver. IEEE Trans. Consumer Electron. 56(4), 2155–2160 (2010).

  106. 106

    Y-H You, K-T Lee, Accurate pilot-aided sampling frequency offset estimation scheme for DRM broadcasting systems. IEEE Trans. Broadcast. 56(4), 558–563 (2010).

  107. 107

    Y-H You, K-W Kwon, Multiplication-free estimation of integer frequency offset for OFDM-based DRM systems. IEEE Signal Process. Lett. 17(10), 851–854 (2010).

  108. 108

    M Morelli, H Lin, Esprit-based carrier frequency offset estimation for OFDM direct-conversion receivers. IEEE Commun. Lett. 17(8), 1513–1516 (2013).

  109. 109

    J Gonzalez-Bayon, A Fernandez-Herrero, C Carreras, Improved schemes for tracking residual frequency offset in DVB-T systems. IEEE Trans. Consumer Electron. 56(2), 415–422 (2010).

  110. 110

    D Bai, W Nam, J Lee, I Kang, Comments on “a technique for orthogonal frequency division multiplexing frequency offset correction”. IEEE Trans. Commun. 61(5), 2109–2111 (2013).

  111. 111

    S-L Su, Y-C Lin, Y-J Fan, Joint sector identity and integer part of carrier frequency offset detection by phase-difference in long term evolution cell search process. IET Commun. 7(10), 950–959 (2013).

  112. 112

    X Wang, F Ye, J Ren, Comments on “estimation of carrier frequency offset with I/Q mismatch using pseudo-offset injection in ofdm systems”. IEEE Trans. Circ. Syst.s I: Regular Papers. 59(11), 2795–2798 (2012).

  113. 113

    C Chu, I Lai, Y Lan, T Chiueh, Efficient sequential integer CFO and sector identity detection for LTE cell search. IEEE Wireless Commun. Lett. 3(4), 389–392 (2014).

  114. 114

    H Lee, J Lee, Joint clock and frequency synchronization for OFDM-based cellular systems. IEEE Signal Process. Lett. 18(12), 757–760 (2011).

  115. 115

    L Marchetti, R Reggiannini, Impact of pilot pattern on carrier frequency recovery for TETRA-like multitone modulations. Electron. Lett. 50(13), 961–963 (2014).

  116. 116

    ES Kang, H Hwang, D-S Han, A fine carrier recovery algorithm robustto doppler shift for OFDM systems. IEEE Trans. Consumer Electron. 56(3), 1218–1222 (2010).

  117. 117

    C-W Chang, Y-H Chung, S-M Phoong, Y-P Lin, in Proc. IEEE PIMRC. Joint estimation of CFO and receiver I/Q imbalance using virtual subcarriers for OFDM systems, (2012), pp. 2297–2302.

  118. 118

    L Bai, Q Yin, CRB for carrier frequency offset estimation with pilot and virtual subcarriers. IEEE Commun. Lett. 16(4), 522–525 (2012).

  119. 119

    A Al-Bassiouni, M Ismail, W Zhuang, An eigenvalue based carrier frequency offset estimator for OFDM systems. IEEE Wireless Commun. Lett. 2(5), 475–478 (2013).

  120. 120

    J Yuan, M Torlak, Modeling and estimation of transient carrier frequency offset in wireless transceivers. IEEE Trans. Wireless Commun. 13(7), 4038–4049 (2014).

  121. 121

    K Lee, S-H Moon, S Kim, I Lee, Sequence designs for robust consistent frequency-offset estimation in OFDM systems. IEEE Trans. Veh. Technol. 62(3), 1389–1394 (2013).

  122. 122

    J Lim, D Hong, Gaussian particle filtering approach for carrier frequency offset estimation in OFDM systems. IEEE Signal Process. Lett. 20(4), 367–370 (2013).

  123. 123

    M Morelli, M Moretti, Carrier frequency offset estimation for OFDM direct-conversion receivers. IEEE Trans. Wireless Commun. 11(7), 2670–2679 (2012).

  124. 124

    Y-H Kim, J-H Lee, Joint maximum likelihood estimation of carrier and sampling frequency offsets for OFDM systems. IEEE Trans. Broadcast. 57(2), 277–283 (2011).

  125. 125

    Y-Y Wang, A subspace-based CFO estimation algorithm for general ICI self-cancelation precoded OFDM systems. IEEE Trans. Wireless Commun. 12(8), 4110–4117 (2013).

  126. 126

    M Morelli, M Moretti, A SAGE approach to frequency recovery in OFDM direct-conversion receivers. IEEE Commun. Lett. 18(4), 536–539 (2014).

  127. 127

    M Morelli, M Moretti, Joint maximum likelihood estimation of CFO, noise power, and SNR in OFDM systems. IEEE Wireless Commun. Lett. 2(1), 42–45 (2013).

  128. 128

    M Morelli, M Moretti, in Proc. IEEE GLOBECOM. Frequency offset estimation in I/Q mismatched OFDM receivers, (2010).

  129. 129

    R Kume, H Lin, K Yamashita, in Proc. IEEE ICC. Repeated preamble based carrier frequency offset estimation in the presence of I/Q imbalance, (2012), pp. 4867–4871.

  130. 130

    G Dainelli, V Lottici, M Moretti, R Reggiannini, in Proc. IEEE ICC. Combined PA/NPA CFO recovery for FBMC transmissions over doubly-selective fading channels, (2011).

  131. 131

    L He, Z Wang, F Yang, S Chen, L Hanzo, Preamble design using embedded signaling for OFDM broadcast systems based on reduced-complexity distance detection. IEEE Trans. Veh. Technol. 60(3), 1217–1222 (2011).

  132. 132

    Y-R Tsai, T-W Wu, in Proc. IEEE VTC Spring. Low-complexity iterative carrier frequency offset estimation with ICI elimination for OFDM systems, (2010).

  133. 133

    K Guo, W Xu, G Zhou, in Proc. IEEE VTC Spring. Differential carrier frequency offset and sampling frequency offset estimation for 3GPP LTE, (2011).

  134. 134

    H Miyashita, M Inamori, Y Sanada, T Ide, in Proc. IEEE VTC Spring. IQ imbalance estimation scheme with intercarrier interference self-cancelation pilot symbols in OFDM direct conversion receivers, (2012).

  135. 135

    B Xie, W Qiu, H Minn, Exact signal model and new carrier frequency offset compensation scheme for OFDM. IEEE Trans. Wireless Commun. 11(2), 550–555 (2012).

  136. 136

    V Dwivedi, G Singh, Repeated correlative coding scheme for mitigation of inter-carrier interference in an orthogonal frequency division multiplexing system. IET Commun.6(6), 599–603 (2012).

  137. 137

    Z Zhang, J Liu, K Long, Low-complexity cell search with fast PSS identification in LTE. IEEE Trans. Veh. Technol. 61(4), 1719–1729 (2012).

  138. 138

    C Ma, H Cao, P Lin, A low-power low-cost design of primary synchronization signal detection. IEEE Trans. Very Large Scale Integration (VLSI) Syst. 20(7), 1161–1166 (2012).

  139. 139

    Q Shi, ICI Mitigation for OFDM Using PEKF. IEEE Signal Process. Lett. 17(12), 981–984 (2010).

  140. 140

    H-G Yeh, K Yao, in Proc. IEEE GLOBECOM. A parallel ICI cancellation technique for OFDM systems, (2012), pp. 3679–3684.

  141. 141

    B Smida, in Proc. IEEE GLOBECOM. Reduction of the peak interference to carrier ratio of OFDM signals, (2011), pp. 1–5.

  142. 142

    M-W Wen, X Cheng, X Wei, B Ai, B-L Jiao, in Proc. IEEE GLOBECOM. A novel effective ICI self-cancellation method, (2011).

  143. 143

    D Li, Y Li, H Zhang, L Cimini, Y Fang, Integer frequency offset estimation for OFDM systems with residual timing offset over frequency selective fading channels. IEEE Trans. Vehicular Technol. 61(6), 2848–2853 (2012).

  144. 144

    W Li, Y Zhang, L-K Huang, C Maple, J Cosmas, Implementation and co-simulation of hybrid pilot-aided channel estimation with decision feedback equalizer for ofdm systems. IEEE Trans. Broadcast. 58(4), 590–602 (2012).

  145. 145

    C Thein, M Fuhrwerk, J Peissig, in Proc. IEEE PIMRC. About the use of different processing domains for synchronization in non-contiguous FBMC systems, (2013), pp. 791–795.

  146. 146

    C Thein, M Fuhrwerk, J Peissig, in Proc. IEEE SPAWC. Frequency-domain processing for synchronization and channel estimation in OQAM-OFDM systems, (2013), pp. 634–638.

  147. 147

    D Van Welden, H Steendam, M Moeneclaey, Iterative decision-directed joint frequency offset and channel estimation for KSP-OFDM. IEEE Trans. Commun. 60(10), 3103–3110 (2012).

  148. 148

    M Rotoloni, S Tomasin, L Vangelista, Maximum likelihood estimation of time and carrier frequency offset for DVB-T2. IEEE Trans. Broadcast. 58(1), 77–86 (2012).

  149. 149

    J-W Choi, J Lee, Q Zhao, H-L Lou, Joint ML estimation of frame timing and carrier frequency offset for OFDM systems employing time-domain repeated preamble. IEEE Trans. Wireless Commun. 9(1), 311–317 (2010).

  150. 150

    G Liu, SV Zhidkov, A composite pn-correlation based synchronizer for TDS-OFDM receiver. IEEE Trans. Broadcast. 56(1), 77–85 (2010).

  151. 151

    T Wiegand, J Rust, S Paul, in Proc. IEEE PIMRC. Reconfigurable architecture of a hybrid synchronisation algorithm for LTE, (2012), pp. 2291–2296.

  152. 152

    H Abdzadeh-Ziabari, MG Shayesteh, Robust timing and frequency synchronization for OFDM systems. IEEE Trans. Vehicular Technol. 60(8), 3646–3656 (2011).

  153. 153

    L He, F Yang, C Zhang, Z Wang, Synchronization for TDS-OFDM over multipath fading channels. IEEE Trans. Consumer Electron. 56(4), 2141–2147 (2010).

  154. 154

    A Viemann, A Waadt, C Spiegel, C Kocks, A Burnic, P Jung, G Bruck, J Kim, J Lim, H Lee, Implementation-friendly synchronisation algorithm for DVB-T2. Electron. Lett. 46(4), 282–283 (2010).

  155. 155

    W Xu, K Manolakis, in Proc. IEEE GLOBECOM. Robust synchronization for 3GPP LTE system, (2010).

  156. 156

    J-C Lin, Initial synchronization assisted by inherent diversity over time-varying frequency-selective fading channels. IEEE. Trans. Wireless Commun. 13(5), 2518–2529 (2014).

  157. 157

    K Xu, Y Xu, W Ma, W Xie, D Zhang, Time and frequency synchronization for multicarrier transmission on hexagonal time-frequency lattice. IEEE Trans. Signal Process. 61(24), 6204–6219 (2013).

  158. 158

    P Udupa, O Sentieys, P Scalart, in Proc. IEEE VTC Spring. A novel hierarchical low complexity synchronization method for OFDM systems, (2013).

  159. 159

    A Goldsmith, Wireless Communications (Cambrige University Press, 2005).

  160. 160

    J Jiang, JS Thompson, H Sun, PM Grant, Practical analysis of codebook design and frequency offset estimation for virtual-multiple-input-multipleoutput systems. IET Commun.7(6), 585–594 (2013).

  161. 161

    X Zhang, H-G Ryu, Joint estimation and suppression of phase noise and carrier frequency offset in multiple-input multiple-output single carrier frequency division multiple access with single-carrier space frequency block coding. IET Commun. 4(16), 1998–2007 (2010).

  162. 162

    J Zhang, YR Zheng, C Xiao, K Ben Letaief, Channel equalization and symbol detection for single-carrier MIMO systems in the presence of multiple carrier frequency offsets. IEEE Trans. Vehicular Technol. 59(4), 2021–2030 (2010).

  163. 163

    J Du, Y-C Wu, Network-wide distributed carrier frequency offsets estimation and compensation via belief propagation. IEEE Trans. Signal Process. 61(23), 5868–5877 (2013).

  164. 164

    J Gao, X Zhu, H Lin, AK Nandi, in Proc. IEEE ICC. Linear least squares cfo estimation and kalman filtering based I/Q imbalance compensation in mimo sc-fde systems, (2010).

  165. 165

    S Sinha, B Shahrrava, G Deep, in Proc. IEEE VTC Fall. A new ml detector for SIMO systems with imperfect channel and carrier frequency offset estimation, (2013).

  166. 166

    B Yao, W Wang, Q Yin, in Proc. IEEE WCNC. Joint AOD and CFO estimation in wireless sensor networks localization system, (2011), pp. 2054–2058.

  167. 167

    M Mohammadkarimi, OA Dobre, Blind identification of spatial multiplexing and alamouti space-time block code via kolmogorov-smirnov (k-s) test. IEEE Commun. Lett. 18(10), 1711–1714 (2014).

  168. 168

    M Marey, OA Dobre, R Inkol, Classification of space-time block codes based on second-order cyclostationarity with transmission impairments. IEEE Trans. Wireless Commun. 11(7), 2574–2584 (2012).

  169. 169

    YA Eldemerdash, OA Dobre, M Marey, GK Karagiannidis, B Liao, in Proc. IEEE GLOBECOM. An efficient algorithm for space-time block code classification, (2013), pp. 3329–3334.

  170. 170

    AA Nasir, S Durrani, RA Kennedy, Blind timing and carrier synchronisation in distributed multiple input multiple output communication systems. IET Commun.5(7), 1028–1037 (2011).

  171. 171

    H Mehrpouyan, AA Nasir, SD Blostein, T Eriksson, GK Karagiannidis, T Svensson, Joint estimation of channel and oscillator phase noise in MIMO systems. EEE Trans. Signal Process. 60(9), 4790–4807 (2012).

  172. 172

    J-S Baek, J-S Seo, Effective symbol timing recovery based on pilot-aided channel estimation for MISO transmission mode of dvb-t2 system. IEEE Trans. Broadcast. 56(2), 193–200 (2010).

  173. 173

    J-S Baek, J-S Seo, Improved CIR-based receiver design for DVB-T2 system in large delay spread channels: Synchronization and equalization. IEEE Trans. Broadcast. 57(1), 103–113 (2011).

  174. 174

    R Mahesh, AK Chaturvedi, in Proc. IEEE WCNC. Fractional timing offset and channel estimation for MIMO OFDM systems over flat fading channels, (2012), pp. 322–325.

  175. 175

    B-S Kim, K Choi, FADAC-OFDM: Frequency-asynchronous distributed alamouti-coded OFDM. IEEE Trans. Veh. Technol. 64(2), 466–480 (2015).

  176. 176

    K Kwon, J Seo, Y Cho, J Paik, Integer frequency offset estimation by pilot subset selection for OFDM system with CDD. Electron. Lett. 48(22), 1434–1435 (2012).

  177. 177

    E-S Shim, H-K Song, Y-H You, Pilot subset partitioning based integer frequency offset estimation for OFDM systems with cyclic delay diversity. IEEE Trans. Broadcast. 56(4), 564–569 (2010).

  178. 178

    S Younis, A Al-Dweik, A Hazmi, B Sharif, C Tsimenidis, Blind carrier frequency offset estimator for multi-input multi-output-orthogonal frequency division multiplexing systems over frequency-selective fading channels. IET Commun.4(8), 990–999 (2010).

  179. 179

    Y Yu, Y Liang, Joint carrier frequency offset and channel estimation for MIMO-OFDM systems using extended H filter. IEEE Commun. Lett. 16(4), 476–478 (2012).

  180. 180

    A Bannour, Y Sun, ML Ammari, F Delestre, R Bouallegue, A novel algebraic carrier frequency offset estimator for ASTC-MIMO-OFDM systems over a correlated frequency-selective channel. IEEE Trans. Vehicular Technol. 61(6), 2468–2475 (2012).

  181. 181

    C Prieto del Amo, MJ Fernandez-Getino Garcia, Iterative joint estimation procedure for channel and frequency offset in multi-antenna OFDM systems with an insufficient cyclic prefix. IEEE Trans. Vehicular Technol. 62(8), 3653–3662 (2013).

  182. 182

    W Zhang, Q Yin, Blind maximum likelihood carrier frequency offset estimation for OFDM with multi-antenna receiver. IEEE Trans. Signal Processi. 61(9), 2295–2307 (2013).

  183. 183

    EP Simon, L Ros, H Hijazi, J Fang, DP Gaillot, M Berbineau, “Joint carrier frequency offset and fast time-varying channel estimation for MIMO-OFDM systems. IEEE Trans. Veh. Technol. 60(3), 955–965 (2011).

  184. 184

    Y-J Liang, J-F Chang, Noniterative joint frequency offset and channel estimation for MIMO OFDM systems using cascaded orthogonal pilots. IEEE Trans. Veh. Technol. 59(8), 4151–4156 (2010).

  185. 185

    W Zhang, Q Yin, W Wang, F Gao, One-shot blind CFO and channel estimation for OFDM with multi-antenna receiver. IEEE Trans. Signal Process. 62(15), 3799–3808 (2014).

  186. 186

    H Nguyen-Le, T Le-Ngoc, NH Tran, Iterative receiver design with joint doubly selective channel and CFO estimation for coded MIMO-OFDM transmissions. IEEE Trans. Veh. Technol. 60(8), 4052–4057 (2011).

  187. 187

    KJ Kim, TA Tsiftsis, R Schober, Semiblind iterative receiver for coded MIMO-OFDM systems. IEEE Trans. Vehicular Technol. 60(7), 3156–3168 (2011).

  188. 188

    E-S Jeon, J Seo, J Yang, DK Kim, Iterative joint detection, ICI cancelation and estimation of multiple CFOs and channels for DVB-T2 in MISO transmission mode. IEEE Trans. Broadcast. 60(1), 29–37 (2014).

  189. 189

    Y-H Chung, S-M Phoong, Joint estimation of I/Q imbalance, CFO and channel response for MIMO OFDM systems. IEEE Trans. Commun. 58(5), 1485–1492 (2010).

  190. 190

    K Choi, Inter-carrier interference-free alamouti-coded OFDM for cooperative systems with frequency offsets in non-selective fading environments. IET Commun.5(15), 2125–2129 (2011).

  191. 191

    B Narasimhan, S Narayanan, H Minn, N Al-Dhahir, Reduced-complexity baseband compensation of joint Tx/Rx I/Q imbalance in mobile MIMO-OFDM. IEEE Trans. Wireless Commun.9(5), 1720–1728 (2010).

  192. 192

    O Weikert, in Proc. IEEE SPAWC. Joint estimation of carrier and sampling frequency offset, phase noise, IQ offset and MIMO channel for lte advanced ul mimo, (2013), pp. 520–524.

  193. 193

    Y Jiang, X Zhu, EG Lim, H Lin, Y Huang, in Proc. IEEE GLOBECOM. Semi-blind MIMO OFDM systems with precoding aided CFO estimation and ICA based equalization, (2013), pp. 3499–3503.

  194. 194

    P Xu, J Wang, Y Han, F Qi, in Proc. IEEE WCNC. H-infinity channel estimation for MIMO-OFDM systems in the presence of carrier frequency offset, (2013), pp. 2926–2931.

  195. 195

    M Lei, M Zhao, J Zhong, Y Cai, in Proc. IEEE VTC Fall. A novel frequency offset tracking algorithm for space-time block coded OFDM systems, (2011).

  196. 196

    J Luo, W Keusgen, A Kortke, in Proc. IEEE VTC Fall. Preamble based joint cfo, frequency-selective i/q-imbalance and channel estimation and compensation in mimo ofdm systems, (2011).

  197. 197

    Q Wang, C Mehlfuhrer, M Rupp, in Proc. IEEE PIMRC. Carrier frequency synchronization in the downlink of 3GPP LTE, (2010), pp. 939–944.

  198. 198

    Q Jing, C Qingchun, S Feifei, E2KF based joint multiple CFOs and channel estimate for MIMO-OFDM systems over high mobility scenarios. China Commun.11(13), 56–63 (2014).

  199. 199

    S Salari, M Heydarzadeh, Joint maximum-likelihood estimation of frequency offset and channel coefficients in multiple-input multiple-output orthogonal frequency-division multiplexing systems with timing ambiguity. IET Commun.5(14), 1964–1970 (2011).

  200. 200

    G Liu, J Ge, Y Guo, Time and frequency offset estimation for distributed multiple-input multiple-output orthogonal frequency division multiplexing systems. IET Commun.4(6), 708–715 (2010).

  201. 201

    T-Y Hsu, S-Y Cheng, Low-complexity sequential searcher for robust symbol synchronization in OFDM systems. IEEE Trans. Very Large Scale Integration (VLSI) Syst. 20(5), 959–963 (2012).

  202. 202

    R Jose, KVS Hari, Maximum likelihood algorithms for joint estimation of synchronisation impairments and channel in multiple input multiple output-orthogonal frequency division multiplexing system. IET Commun.7(15), 1567–1579 (2013).

  203. 203

    H-C Wang, C-L Wang, in Proc. IEEE VTC Fall. A compact preamble design for synchronization in distributed MIMO OFDM systems, (2011).

  204. 204

    AA Nasir, H Mehrpouyan, S Durrani, SD Blostein, RA Kennedy, B Ottersten, Optimal training sequences for joint timing synchronization and channel estimation in distributed communication networks. IEEE Trans. Commun. 61(7), 3002–3015 (2013).

  205. 205

    I Avram, M Moeneclaey, in Proc. IEEE PIMRC. Quantize and forward cooperative communication: Joint channel and frequency offset estimation, (2012), pp. 845–850.

  206. 206

    H Mehrpouyan, SD Blostein, in Proc. IEEE GLOBECOM. Estimation, training, and effect of timing offsets in distributed cooperative networks, (2010).

  207. 207

    F Wang, M-A Ingram, in Proc. IEEE ICC. A practical equalizer for cooperative delay diversity with multiple carrier frequency offsets, (2012), pp. 4100–4104.

  208. 208

    H Wang, Q Yin, X-G Xia, Full diversity space-frequency codes for frequency asynchronous cooperative relay networks with linear receivers. IEEE Trans. Commun. 59(1), 236–247 (2011).

  209. 209

    T Liu, S Zhu, Joint CFO and channel estimation for asynchronous cooperative communication systems. IEEE Signal Process. Lett. 19(10), 643–646 (2012).

  210. 210

    AA Nasir, S Durrani, RA Kennedy, in Proc. IEEE ICC. Blind timing and carrier synchronization in decode and forward cooperative systems, (2011).

  211. 211

    H-M Wang, Q Yin, X-G Xia, Fast kalman equalization for time-frequency asynchronous cooperative relay networks with distributed space-time codes. IEEE Trans. Veh. Technol. 59(9), 4651–4658 (2010).

  212. 212

    Y Liu, Y Li, D Li, Q Ma, H Zhang, Diversity analysis of distributed linear convolutive space-time codes for time-frequency asynchronous cooperative networks. IET Commun.8(5), 722–729 (2014).

  213. 213

    AA Nasir, H Mehrpouyan, S Durrani, SD Blostein, RA Kennedy, B Ottersten, in Proc. IEEE SPAWC. DSTBC based DF cooperative networks in the presence of timing and frequency offsets, (2013), pp. 86–90.

  214. 214

    M Jain, SL Miller, A Sprintson, in Proc. IEEE GLOBECOM. Parameter estimation and tracking in physical layer network coding, (2011).

  215. 215

    X Li, C Xing, Y-C Wu, SC Chan, Timing estimation and resynchronization for amplify-and-forward communication systems. IEEE Trans. Signal Process. 58(4), 2218–2229 (2010).

  216. 216

    H Mehrpouyan, SD Blostein, Comments on “timing estimation and resynchronization for amplify-and-forward communication systems”. IEEE Trans. Signal Process. 59(8), 4047–4048 (2011).

  217. 217

    A Nasir, H Mehrpouyan, S Durrani, S Blostein, R Kennedy, B Ottersten, “Transceiver design for distributed STBC based AF cooperative networks in the presence of timing and frequency offsets. IEEE Trans. Signal Process. 61(12), 3143–3158 (2013).

  218. 218

    A Yadav, V Tapio, M Juntti, J Karjalainen, in Proc. IEEE ICC. Timing and frequency offsets compensation in relay transmission for 3GPP LTE uplink, (2010).

  219. 219

    Z Wu, G Li, T Wang, in Proc. IEEE ICC. Differential modulation for amplify-and-forward two-way relaying with carrier offsets, (2014), pp. 4501–4506.

  220. 220

    AA Nasir, H Mehrpouyan, S Durrani, S Blostein, RA Kennedy, in Proc. IEEE SPAWC. Training-based synchronization and channel estimation in AF two-way relaying network, (2014).

  221. 221

    S Chang, B Kelley, An efficient time synchronization scheme for broadband two-way relaying networks based on physical-layer network coding. IEEE Commun. Lett. 16(9), 1416–1419 (2012).

  222. 222

    Z Jiang, H Wang, Z Ding, A bayesian algorithm for joint symbol timing synchronization and channel estimation in two-way relay networks. IEEE Trans. Commun. 61(10), 4271–4283 (2013).

  223. 223

    Q Zhao, Z Zhou, J Li, B Vucetic, Joint semi-blind channel estimation and synchronization in two way relay networks. IEEE Trans. Vehicular Technol. 63(7), 3276–3293 (2014).

  224. 224

    T Ferrett, H Ochiai, MC Valenti, in Proc. IEEE VTC Spring. Physical-layer network coding using FSK modulation under frequency offset, (2012).

  225. 225

    Z Zhang, J Liu, K Long, Y Fan, in Proc. IEEE VTC Spring. Frequency offset and channel estimation in co-relay cooperative OFDM systems, (2012).

  226. 226

    KJ Kim, RA Iltis, HV Poor, Frequency offset and channel estimation in cooperative relay networks. IEEE Trans. Vehicular Technol. 60(7), 3142–3155 (2011).

  227. 227

    J Xiong, Q Huang, Y Xi, D Ma, J Wei, Multiple carrier frequency offsets tracking in co-operative space-frequency block-coded orthogonal frequency division multiplexing systems. IET Commun.7(3), 263–269 (2013).

  228. 228

    Q Huang, M Ghogho, D Ma, J Wei, Low-complexity data-detection algorithm in cooperative SFBC-OFDM systems with multiple frequency offsets. IEEE Trans. Veh. Technol. 59(9), 4614–4620 (2010).

  229. 229

    L Thiagarajan, S Sun, PHW Fung, CK Ho, in Proc. IEEE GLOBECOM. Multiple carrier frequency offset and channel estimation for distributed relay networks, (2010).

  230. 230

    L Rugini, P Banelli, in Proc. IEEE SPAWC. Pilot-aided estimation of carrier frequency offsets and channel impulse responses for OFDM cooperative communications, (2012), pp. 550–554.

  231. 231

    C-L Wang, P-C Shen, M-C Bai, H-C Wang, in Proc. IEEE GLOBECOM. An adaptive receiver design for OFDM-based cooperative relay systems using conjugate transmission, (2012), pp. 3644–3648.

  232. 232

    F Etezadi, L Szczecinski, A Ghrayeb, in Proc. IEEE GLOBECOM. Correction of the CFO in OFDM relay-based space-time codes, (2010).

  233. 233

    T-T Lu, H-D Lin, T-H Sang, in Proc. IEEE PIMRC. An SFBC-OFDM receiver to combat multiple carrier frequency offsets in cooperative communications, (2010), pp. 899–904.

  234. 234

    H Lu, T Xu, H Nikookar, in Proc. IEEE VTC Spring. A cooperative scheme for ZP-OFDM with multiple carrier frequency offsets over multipath channel, (2011).

  235. 235

    S Ponnaluri, B Azimi-Sadjadi, D McCarthy, PJ Oleski, in Proc. IEEE GLOBECOM 2010. Cooperative relaying using OFDM in the presence of frequency offsets, (2010).

  236. 236

    J Xiao, Y Jiang, X You, in Proc. IEEE VTC Spring. A low complexity equalization method for cooperative communication systems based on distributed frequency-domain linear convolutive space-frequency codes, (2011).

  237. 237

    H-W Wang, DW Lin, T-H Sang, in Proc. IEEE PIMRC. Detection of cooperative OFDM signals in time-varying channels with partial whitening of intercarrier interference, (2013), pp. 1426–1430.

  238. 238

    Q Huang, M Ghogho, J Wei, P Ciblat, Practical timing and frequency synchronization for OFDM-based cooperative systems. IEEE Trans. Signal Process. 58(7), 3706–3716 (2010).

  239. 239

    Y Guo, G Liu, J Ge, H Ding, Time and frequency synchronisation scheme for orthogonal frequency division multiplexing-based cooperative systems. IET Commun.7(16), 1836–1843 (2013).

  240. 240

    G Yang, C-L Wang, H-C Wang, S-Q Li, in Proc. IEEE GLOBECOM 2010. A new synchronization scheme for OFDM-based cooperative relay systems, (2010).

  241. 241

    J Zhang, C Shen, G Deng, Y Wang, in Proc. IEEE VTC Fall. Timing and frequency synchronization for cooperative relay networks, (2013).

  242. 242

    Y-J Won, B-S Seo, in Proc. IEEE PIMRC. Compensation of multiple carrier frequency offsets in amplify-and-forward cooperative networks, (2013), pp. 159–163.

  243. 243

    W Zhang, F Gao, Q Yin, H Wang, Space-frequency convolutional coding for frequency-asynchronous AF relay networks. IEEE Trans. Vehicular Technol. 61(5), 2412–2418 (2012).

  244. 244

    W Zhang, F Gao, Q Yin, H-M Wang, Alamouti coding scheme for AF relaying with doppler shifts. IEEE Trans. Vehicular Technol. 62(3), 1241–1250 (2013).

  245. 245

    OH Salim, AA Nasir, W Xiang, RA Kennedy, in Proc. IEEE ICC. Joint channel, phase noise, and carrier frequency offset estimation in cooperative OFDM systems, (2014), pp. 4384–4389.

  246. 246

    Y Yao, X Dong, Multiple CFO mitigation in amplify-and-forward cooperative OFDM transmission. IEEE Trans. Commun. 60(12), 3844–3854 (2012).

  247. 247

    Y-H Jung, S Cho, C You, Decentralised ranging method for orthogonal frequency division multiple access systems with amplify-and-forward relays. IET Commun. 8(9), 1609–1615 (2014).

  248. 248

    CK Ho, PHW Fung, S Sun, Carrier frequency offset estimation for two-way relaying: Optimal preamble and estimator design. IEEE Trans. Wireless Commun. 12(4), 1898–1909 (2013).

  249. 249

    G Wang, F Gao, Y-C Wu, C Tellambura, Joint CFO and channel estimation for OFDM-based two-way relay networks. IEEE Trans. Wireless Commun. 10(2), 456–465 (2011).

  250. 250

    X Li, C Xiong, J Feldman, in Proc. IEEE GLOBECOM Wkshps. Ofdm transmission scheme for asynchronous two-way multi-relay cooperative networks with analog network coding, (2013), pp. 19–24.

  251. 251

    X Zhang, H-G Ryu, Suppression of ICI and MAI in SC-FDMA communication system with carrier frequency offsets. IEEE Trans. Consumer Electron. 56(2), 359–365 (2010).

  252. 252

    S Sesia, I Toufik, M Baker, LTE - The UMTS Long Term Evolution: From Theory to Practice (John Wiley & Sons Ltd, 2009).

  253. 253

    SH Song, GL Chen, KB Letaief, Localized or interleaved? a tradeoff between diversity and CFO interference in multipath channels. IEEE Trans. Wireless Commun. 10(9), 2829–2834 (2011).

  254. 254

    M Ma, X Huang, YJ Guo, An interference self-cancellation technique for SC-FDMA systems. IEEE Commun. Lett. 14(6), 512–514 (2010).

  255. 255

    FS Al-Kamali, M Dessouky, B Sallam, F Shawki, W Al-Hanafy, F El-Samie, Joint low-complexity equalization and carrier frequency offsets compensation scheme for MIMO sc-fdma systems. IEEE Trans. Wireless Commun. 11(3), 869–873 (2012).

  256. 256

    Y Zhu, K Ben Letaief, CFO estimation and compensation in SC-IFDMA systems. IEEE Trans. Wireless Commun. 9(10), 3200–3213 (2010).

  257. 257

    N Iqbal, N Al-Dhahir, A Zerguine, A Zidouri, Adaptive frequency-domain RLS DFE for uplink MIMO SC-FDMA. IEEE Trans. Veh. Technol.64(7), 2819–2833 (2015). accepted to appear.

  258. 258

    FS Al-Kamali, Low-complexity joint regularised equalisation and carrier frequency offsets compensation scheme for single-carrier frequency division multiple access system. IET Commun. 8(5), 767–773 (2014).

  259. 259

    F Al-Kamali, MI Dessouky, BM Sallam, F Shawki, FEAbd El-Samie, Uplink single-carrier frequency division multiple access system with joint equalisation and carrier frequency offsets compensation. IET Commun. 5(4), 425–433 (2011).

  260. 260

    K-C Fu, S-W Huang, Y-F Chen, Adaptive schemes and analysis for blind beamforming with insufficient cyclic prefix in single carrierfrequency division multiple access systems. IET Commun. 8(9), 1477–1487 (2014).

  261. 261

    G Chen, Y Zhu, KB Letaief, in Proc. IEEE ICC. Combined MMSE-FDE and interference cancellation for uplink SC-FDMA with carrier frequency offsets, (2010).

  262. 262

    D Darsena, G Gelli, L Paura, F Verde, Blind channel shortening for asynchronous SC-IFDMA systems with CFOs. IEEE Trans. Wireless Commun. 12(11), 5529–5543 (2013).

  263. 263

    KJ Kim, M-O Pun, R Iltis, Joint carrier frequency offset and channel estimation for uplink MIMO-OFDMA systems using parallel schmidt rao-blackwellized particle filters. IEEE Trans. Commun. 58(9), 2697–2708 (2010).

  264. 264

    P Sun, M Morelli, L Zhang, Carrier frequency offset tracking in the IEEE 802.16e OFDMA uplink. IEEE Trans. Wireless Commun. 9(12), 3613–3619 (2010).

  265. 265

    HK Shah, KS Dasgupta, H Soni, Low complexity scheme for carrier frequency offset estimation in orthogonal frequency division multiple access uplink. IET Commun.7(13), 1405–1411 (2013).

  266. 266

    H-K Ho, J-F Kiang, Efficient carrier frequency offset estimation for orthogonal frequency-division multiple access uplink with an arbitrary number of subscriber stations. IET Commun. 8(2), 199–209 (2014).

  267. 267

    H-T Hsieh, WR Wu, Blind maximum-likelihood carrier-frequency-offset estimation for interleaved OFDMA uplink systems. IEEE Trans. Vehicular Technol. 60(1), 160–173 (2011).

  268. 268

    W Zhang, Q Yin, Blind carrier frequency offset estimation for tile-based orthogonal frequency division multiple access uplink with multi-antenna receiver. IET Commun.8(8), 1309–1316 (2014).

  269. 269

    W Zhang, F Gao, Q Yin, A Nallanathan, Blind carrier frequency offset estimation for interleaved OFDMA uplink. IEEE Trans. Signal Process. 60(7), 3616–3627 (2012).

  270. 270

    K Lee, S-H Moon, S-R Lee, I Lee, Low complexity pilot assisted carrier frequency offset estimation for OFDMA uplink systems. IEEE Trans. Wireless Commun. 11(8), 2690–2695 (2012).

  271. 271

    S-W Keum, D-H Kim, H-M Kim, An improved frequency offset estimation based on companion matrix in multi-user uplink interleaved ofdma systems. IEEE Signal Process. Lett. 21(4), 409–413 (2014).

  272. 272

    HC Nguyen, E de Carvalho, R Prasad, Multi-user interference cancellation schemes for carrier frequency offset compensation in uplink ofdma. IEEE Trans. Wireless Commun.13(3), 1164–1171 (2014).

  273. 273

    H Solis-Estrella, AG Orozco-Lugo, Carrier frequency offset estimation in OFDMA using digital filtering. IEEE Wireless Commun. Lett. 2(2), 199–202 (2013).

  274. 274

    L Bai, Q Yin, Frequency synchronization for the OFDMA uplink based on the tile structure of IEEE 802.16e. IEEE Trans. Vehicular Technol. 61(5), 2348–2353 (2012).

  275. 275

    P Muneer, SM Sameer, Pilot-aided joint estimation of doubly selective channel and carrier frequency offsets in OFDMA uplink with high-mobility users. IEEE Trans. Veh. Technol. 64(7), 2819–2833 (2015).

  276. 276

    K Lee, S-R Lee, S-H Moon, I Lee, MMSE-based CFO compensation for uplink OFDMA systems with conjugate gradient. IEEE Trans. Wireless Commun. 11(8), 2767–2775 (2012).

  277. 277

    T Peng, Y Xiao, X He, S Li, Improved detection of uplink OFDM-IDMA signals with carrier frequency offsets. IEEE Commun. Lett. 16(5), 646–649 (2012).

  278. 278

    R Fa, L Zhang, Generalised grouped minimum mean-squared errorbased multi-stage interference cancellation scheme for orthogonal frequency division multiple access uplink systems with carrier frequency offsets. IET Commun.7(7), 685–695 (2013).

  279. 279

    W Hou, X Wang, G Liu, Ml detection with successive group interference cancellation for interleaved OFDMA uplink. IEEE Commun. Lett. 16(1), 34–36 (2012).

  280. 280

    S Yerramalli, M Stojanovic, U Mitra, in Proc. IEEE GLOBECOM. Carrier frequency offset estimation for uplink OFDMA using partial FFT demodulation, (2010).

  281. 281

    B Aziz, I Fijalkow, M Ariaudo, in Proc. IEEE GLOBECOM. Trade off between frequency diversity and robustness to carrier frequency offset in uplink OFDMA system, (2011).

  282. 282

    J Chen, J Chen, Y-C Wu, in Proc. IEEE GLOBECOM. Frequency synchronization for multiuser MIMO-OFDM system using bayesian approach, (2010).

  283. 283

    P Bertrand, in Proc. IEEE VTC Spring. Frequency offset estimation in 3G LTE, (2010).

  284. 284

    D-C Chang, T-H Li, in Proc. IEEE VTC Spring. MMSE solution for OFDMA systems with carrier frequency offset correction, (2010).

  285. 285

    K-H Wu, W-H Fang, Y-T Chen, in Proc. IEEE VTC Spring. Joint carrier frequency offset and direction of arrival estimation via hierarchical ESPRIT for interleaved ofdma/sdma uplink systems, (2010).

  286. 286

    X Zhang, H-G Ryu, J Gao, in Proc. IEEE VTC Spring. A time domain inverse matrix receiver for CFO suppression in WIMAX uplink system, (2010).

  287. 287

    H Xiong, E Bodanese, in Proc. IEEE VTC Fall. A scheme to support concurrent transmissions in OFDMA based ad hoc networks, (2012).

  288. 288

    Y-P Tu, W-H Fang, Y-T Chen, in Proc. IEEE VTC Spring. A novel subspace decomposition-based detection scheme with soft interference cancellation for OFDMA uplink, (2012).

  289. 289

    A Farhang, N Marchetti, L Doyle, in Proc. IEEE ICC. Low complexity LS and MMSE based CFO compensation techniques for the uplink of OFDMA systems, (2013), pp. 5748–5753.

  290. 290

    L Sanguinetti, M Morelli, A low-complexity scheme for frequency estimation in uplink OFDMA systems. 9(8), 2430–2437 (2010).

  291. 291

    L Sanguinetti, M Morelli, An initial ranging scheme for the ieee 802.16 ofdma uplink. IEEE Trans. Wireless Commun. 11(9), 3204–3215 (2012).

  292. 292

    L Sanguinetti, M Morelli, HV Poor. Uplink synchronization in OFDMA spectrum-sharing systems. 58(5), 2771–2782 (2010).

  293. 293

    H-H Chen, Next Genearation CDMA Technologies (John Wiley & Sons Ltd., 2007).

  294. 294

    D Xu, L Huang, X Xu, Z Ye, Widely linear MVDR beamformers for noncircular signals based on time-averaged second-order noncircularity coefficient estimation. IEEE Trans. Veh. Technol. 62(7), 3219–3227 (2013).

  295. 295

    Y Wang, J Coon, MUI-reducing spreading code design for BS-CDMA in the presence of carrier frequency offset. IEEE Trans. Vehicular Technol. 60(6), 2583–2593 (2011).

  296. 296

    B Seo, Sinr lower bound based multiuser detector for uplink MC-CDMA systems with residual frequency offset. IEEE Commun. Lett. 16(10), 1612–1615 (2012).

  297. 297

    L Tadjpour, S-H Tsai, C-C Kuo, Simplified multiaccess interference reduction for MC-CDMA with carrier frequency offsets. IEEE Trans. Veh. Technol.59(5), 2543–2555 (2010).

  298. 298

    T-T Lin, F-H Hwang, MCMOE-based CFO estimator aided with the correlation matrix approach for alamouti’s STBC mc-cdma downlink systems. IEEE Trans. Veh. Technol. 61(8), 3790–3795 (2012).

  299. 299

    G Manglani, AK Chaturvedi, Multi-tone CDMA design for arbitrary frequency offsets using orthogonal code multiplexing at the transmitter and a tunable receiver. IET Commun.5(15), 2157–2166 (2011).

  300. 300

    X Li, R Zhou, S Hong, Z Wu, in Proc. IEEE GLOBECOM. Total inter-carrier interference cancellation for MC-CDMA system in mobile environment, (2010).

  301. 301

    Y Yan, Y Gong, M Ma, Q Shi, Iterative frequency-domain fractionally spaced receiver for zero-padded multi-carrier code division multiple access systems. IET Commun.8(17), 2993–3000 (2014).

  302. 302

    A Goldsmith, SA Jafar, I Maric, S Srinivasa, Breaking spectrum gridlock with cognitive radios: An information theoretic perspective. Proc. IEEE. 97(5), 894–914 (2009).

  303. 303

    J Guo, S Durrani, X Zhou, Performance analysis of arbitrarily-shaped underlay cognitive networks: Effects of secondary user activity protocols. IEEE Trans. Commun. 63(2), 376–389 (2015).

  304. 304

    Z Xu, C Yang, Secondary transceiver design in the presence of frequency offset between primary and secondary systems. IEEE Trans. Wireless Commun. 9(11), 3461–3471 (2010).

  305. 305

    R Zhou, X Li, V Chakarvarthy, Z Wu, in Proc. IEEE GLOBECOM. Software defined radio implementation of SMSE based overlay cognitive radio in high mobility environment, (2011).

  306. 306

    E Rebeiz, P Urriza, D Cabric, Optimizing wideband cyclostationary spectrum sensing under receiver impairments. IEEE Trans. Signal Process. 61(15), 3931–3943 (2013).

  307. 307

    I Nevat, G Peters, J Yuan, in Proc. IEEE WCNC. Blind spectrum sensing in cognitive radio over fading channels and frequency offsets, (2012), pp. 1039–1043.

  308. 308

    Y Zeng, Y-C Liang, T-H Pham, Spectrum sensing for OFDM signals using pilot induced auto-correlations. IEEE J. Selected Areas Commun. 31(3), 353–363 (2013).

  309. 309

    J Ding, E Dutkiewicz, X Huang, D Qu, Jiang T, in Proc. IEEE GLOBECOM. Carrier frequency offset estimation for non-contiguous OFDM receiver in cognitive radio systems, (2013), pp. 4192–4197.

  310. 310

    P Zhao, C Shen, in Proc. IEEE GLOBECOM. A low-delay low-complexity EKF design for joint channel and CFO estimation in multi-user cognitive communications, (2011).

  311. 311

    M Zivkovic, R Mathar, in Proc. IEEE GLOBECOM. Joint frequency synchronization and spectrum occupancy characterization in OFDM-based cognitive radio systems, (2011).

  312. 312

    E Axell, EG Larsson, Eigenvalue-based spectrum sensing of orthogonal space-time block coded signals. IEEE Trans. Signal Process. 60(12), 6724–6728 (2012).

  313. 313

    Z Chen, T Luan, X-D Zhang, Sensing orthogonal frequency division multiplexing systems for cognitive radio with cyclic prefix and pilot tones. IET Commun. 6(1), 97–106 (2012).

  314. 314

    A Al-Habashna, OA Dobre, R Venkatesan, DC Popescu, Second-order cyclostationarity of mobile WiMAX and LTE OFDM signals and application to spectrum awareness in cognitive radio systems. IEEE J. Selected Topics Signal Process. 6(1), 26–42 (2012).

  315. 315

    P Cheraghi, Y Ma, R Tafazolli, Z Lu, Cluster-based differential energy detection for spectrum sensing in multi-carrier systems. IEEE Trans. Signal Process. 60(12), 6450–6464 (2012).

  316. 316

    J Liu, X Wang, J-Y Chouinard, in Proc. IEEE VTC Spring. Iterative blind OFDM parameter estimation and synchronization for cognitive radio systems, (2012).

  317. 317

    T Liu, S Zhu, F Gao, A simplified MMSE equalizer for distributed TR-STBC systems with multiple CFOs. IEEE Commun. Lett. 16(8), 1300–1303 (2012).

  318. 318

    M Caus, AI Perez-Neira, in Proc. IEEE SPAWC. Interference mitigation techniques for asynchronous multiple access communications in SIMO FBMC systems, (2011), pp. 331–335.

  319. 319

    Y Zhi, S Gang, W Xin, in Proc. IEEE VTC Spring. A novel initial cell search scheme in TD-LTE, (2011).

  320. 320

    F Sanchez, T Zemen, G Matz, F Kaltenberger, N Czink, in Proc. IEEE ICC. Cooperative space-time coded ofdm with timing errors and carrier frequency offsets, (2011).

  321. 321

    R Rogalin, OY Bursalioglu, H Papadopoulos, G Caire, AF Molisch, A Michaloliakos, V Balan, K Psounis, Scalable synchronization and reciprocity calibration for distributed multiuser MIMO. IEEE Trans. Wireless Commun. 13(4), 1815–1831 (2014).

  322. 322

    D Lee, H Seo, B Clerckx, E Hardouin, D Mazzarese, S Nagata, K Sayana, Coordinated multipoint transmission and reception in lte-advanced: deployment scenarios and operational challenges. Commun. Mag. IEEE. 50(2), 148–155 (2012).

  323. 323

    F Silva, R Dinis, P Montezuma, Channel estimation and equalization for asynchronous single frequency networks. IEEE Trans. Broadcast. 60(1), 110–119 (2014).

  324. 324

    L Zhao, K Liang, G Cao, R Qian, D Lopez-Perez, An enhanced signal-timing-offset compensation algorithm for coordinated multipoint-to-multiuser systems. IEEE Commun. Lett. 18(6), 983–986 (2014).

  325. 325

    S Iwelski, B Badic, Z Bai, R Balraj, C Kuo, E Majeed, T Scholand, G Bruck, P Jung, Feedback generation for CoMP transmission in unsynchronized networks with timing offset. IEEE Commun. Lett. 18(5), 725–728 (2014).

  326. 326

    B Zarikoff, J Cavers, Coordinated multi-cell systems: Carrier frequency offset estimation and correction. IEEE J. Selected Areas Commun. 28(9), 1490–1501 (2010).

  327. 327

    Y-J Liang, G Stuber, J-F Chang, D-N Yang, A joint channel and frequency offset estimator for the downlink of coordinated MIMO-OFDM systems. IEEE Trans. Wireless Commun. 11(6), 2254–2265 (2012).

  328. 328

    Y Jiang, X Zhu, E Lim, Y Huang, H Lin, Low-complexity semiblind multi-CFO estimation and ICA-based equalization for CoMP OFDM systems. IEEE Trans. Veh. Technol. 63(4), 1928–1934 (2014).

  329. 329

    Y-R Tsai, H-Y Huang, Y-C Chen, K-J Yang, Simultaneous multiple carrier frequency offsets estimation for coordinated multi-point transmission in ofdm systems. IEEE Trans. Wireless Commun. 12(9), 4558–4568 (2013).

  330. 330

    V Vakilian, T Wild, F Schaich, S ten Brink, J-F Frigon, in Proc. IEEE GLOBECOM Wkshps. Universal-filtered multi-carrier technique for wireless systems beyond LTE, (2013), pp. 223–228.

  331. 331

    R Pec, BW Ku, KS Kim, YS Cho, Receive beamforming techniques for an LTE-based mobile relay station with a uniform linear array. IEEE Trans. Veh. Technol. 64(7), 3299–3304 (2015).

  332. 332

    Y Liu, Y Li, D Li, H Zhang, in Proc. IEEE WCNC. Space-time coding for time and frequency asynchronous CoMP transmissions, (2013), pp. 2632–2637.

  333. 333

    T Koivisto, T Kuosmanen, T Roman, in Proc. IEEE VTC Fall. Estimation of time and frequency offsets in LTE coordinated multi-point transmission, (2013).

  334. 334

    E Mochida, M Hirakawa, T Yamamoto, Y Tanaka, Y Hamada, Y Okada, M Sugimoto, in Proc. IEEE VTC Spring. Resource block basis MMSE beamforming for interference suppression in LTE uplink, (2012).

  335. 335

    F Coelho, R Dinis, P Montezuma, in Proc. IEEE VTC Fall. Receiver design for single-frequency networks with fast-varying channels, (2011).

  336. 336

    J-H Deng, S-M Liao, Robust carrier frequency offset and channel estimation for orthogonal frequency division multiple access downlink systems in the presence of severe adjacent-cell interference. IET Commun. 8(1), 58–68 (2014).

  337. 337

    Y-C Hung, S-Y Peng, S-HL Tsai, Sequence designs for interference mitigation in multi-cell networks. IEEE Trans. Wireless Commun. 13(1), 394–406 (2014).

  338. 338

    L Yang, G Ren, W Zhai, Z Qiu, Beamforming based receiver scheme for DVB-T2 system in high speed train environment. IEEE Trans. Broadcast. 59(1), 146–154 (2013).

  339. 339

    S-F Liu, P-Y Tsai, in Proc. IEEE WCNC. A non-coherent neighbor cell search scheme for LTE/LTE-A systems, (2013), pp. 3300–3305.

  340. 340

    M Morelli, L Marchetti, M Moretti, Maximum likelihood frequency estimation and preamble identification in OFDMA-based WiMAX systems. IEEE Trans. Wireless Commun. 13(3), 1582–1592 (2014).

  341. 341

    K Chang, P Ho, Y Choi, Signal design for reduced complexity and accurate cell search/synchronization in OFDM-based cellular systems. IEEE Trans. Veh. Technol. 61(9), 4170–4175 (2012).

  342. 342

    S Roy, JR Foerster, VS Somayazulu, DG Leeper, Ultrawideband radio design: The promise of high-speed, short-range wireless connectivity. Proceedings of the IEEE. 92(2), 1407–1419 (2004).

  343. 343

    GE Bottomley, T Ottosson, Y-P Wang, A generalized RAKE receiver for interference suppression. IEEE J. Selected Areas Commun. 18(8), 1536–1545 (2000).

  344. 344

    Y Li, H Minn, P Sadeghi, RMAP Rajatheva, Synchronization, channel estimation, and equalization in MB-OFDM systems. IEEE Trans. Wireless Commun. 7(11), 1–12 (2008).

  345. 345

    S Chen, L Wang, G Chen, Data-aided timing synchronization for FM-DCSK UWB communication systems. IEEE Trans. Ind. Electron. 57(5), 1538–1545 (2010).

  346. 346

    T Lv, Y Qiao, Z Wang, Training-based synchronization and demodulation with low complexity for UWB signals. IEEE Trans. Vehicular Technol. 60(8), 3736–3747 (2011).

  347. 347

    T Erseghe, A Cipriano, Maximum likelihood frequency offset estimation in multiple access time-hopping UWB. IEEE Trans. Wireless Commun. 10(7), 2040–2045 (2011).

  348. 348

    T Erseghe, Schmidl-cox-like frequency offset estimation in time-hopping UWB. IEEE Trans. Wireless Commun. 10(12), 4041–4047 (2011).

  349. 349

    M-K Oh, J-Y Kim, H Lee, Traffic-reduced precise ranging protocol for asynchronous UWB positioning networks. IEEE Commun. Lett. 14(5), 432–434 (2010).

  350. 350

    Y-H You, S Lee, Improved frequency offset estimation scheme for UWB systems with cyclic delay diversity. IEEE Trans. Consumer Electron. 57(3), 1079–1084 (2011).

  351. 351

    J-Y Kim, Y-H You, T Hwang, Blind frequency-offset tracking scheme for multiband orthogonal frequency division multiplexing using time-domain spreading. IET Commun. 5(11), 1544–1549 (2011).

  352. 352

    Z Lin, X Peng, K-B Png, F Chin, Iterative sampling frequency offset estimation for MB-OFDM UWB systems with long transmission packet. IEEE Trans. Veh. Technol. 61(4), 1685–1697 (2012).

  353. 353

    A Karim, M Othman, Improved fine CFO synchronization for MB-OFDM UWB. IEEE Commun. Lett. 14(4), 351–353 (2010).

  354. 354

    Z Wang, Y Xin, G Mathew, X Wang, Efficient phase-error suppression for multiband OFDM-based UWB systems. IEEE Trans. Vehicular Technol. 59(2), 766–778 (2010).

  355. 355

    SJ Hwang, Y Han, S-W Kim, J Park, B-G Min, Resource efficient implementation of low power MB-OFDM PHY baseband modem with highly parallel architecture. IEEE Trans. Very Large Scale Integration (VLSI) Syst. 20(7), 1248–1261 (2012).

  356. 356

    AA D’Amico, L Taponecco, U Mengali, Ultra-wideband TOA estimation in the presence of clock frequency offset. IEEE Trans. Wireless Commun. 12(4), 1606–1616 (2013).

  357. 357

    Z Ye, C Duan, PV Orlik, J Zhang, AA Abouzeid, A synchronization design for UWB-based wireless multimedia systems. IEEE Trans. Broadcast. 56(2), 211–225 (2010).

  358. 358

    W Fan, C-S Choy, Robust, low-complexity, and energy efficient downlink baseband receiver design for MB-OFDM UWB system. IEEE Trans. Circ. Syst. I: Regular Papers. 59(2), 399–408 (2012).

  359. 359

    T Kohda, Y Jitsumatsu, K Aihara, in Proc. IEEE VTC Fall. Gabor division/spread spectrum system is separable in time and frequency synchronization, (2013).

  360. 360

    F Benedetto, G Giunta, A fast time-delay estimator of PN signals. IEEE Trans. Commun. 59(8), 2057–2062 (2011).

  361. 361

    D Oh, M Kwak, J-W Chong, A subspace-based two-way ranging system using a chirp spread spectrum modem, robust to frequency offset. IEEE Trans. Wireless Commun. 11(4), 1478–1487 (2012).

  362. 362

    WM Jang, L Chi, Self-encoded multi-carrier spread spectrum with iterative despreading for random residual frequency offset. J. Commun. Netw. 15(3), 258–265 (2013).

  363. 363

    T Kohd, Y Jitsumats, K Aihara, in Proc. IEEE GLOBECOM. Frequency-division spread-spectrum makes frequency synchronisation easy, (2012), pp. 3952–3958.

  364. 364

    KJ Zou, KW Yang, Network synchronization for dense small cell networks. IEEE Wireless Commun.22(2), 108–117 (2015).

  365. 365

    S Won, L Hanzo, Synchronization issues in relay-aided cooperative MIMO networks. IEEE Wireless Commun.21(5), 41–51 (2014).

  366. 366

    H Mehrpouyan, MR Khanzadi, M Matthaiou, AM Sayeed, R Schober, Y Hua, Improving bandwidth efficiency in e-band communication systems. IEEE Commun. Mag. 52(3) (2014).

  367. 367

    TS Rappaport, RW Heath, RC Daniels, N Murdock, Millimeter-wave wireless communications (Prentice Hall, 2014).

  368. 368

    A Ulusoy, S Krone, G Liu, A Trasser, F Guderian, B Almeroth, A Barghouthi, M Hellfeld, S Schumann, C Carta, C Estañ, K Dombrowski, V Brankovic, D Radovic, F Ellinger, G Fettweis, H Schumacher, in Silicon Monolithic Integrated Circuits in RF Systems (SiRF), 2013 IEEE 13th Topical Meeting on. A 60 GHz multi-Gb/s system demonstrator utilizing analog synchronization and 1-bit data conversion, (2013), pp. 99–101.

  369. 369

    CN Barati, SA Hosseini, S Rangan, P Liu, T Korakis, SS Panwar, TS Rappaport, Directional cell discovery in millimeter wave cellular networks. IEEE Trans. Wireless Commun. 14(12), 6664–6678 (2015).

  370. 370

    L Koschel, A Kortke, in Proc. IEEE PIMRC. Frequency synchronization and phase offset tracking in a real-time 60-GHz CS-OFDM MIMO system, (2012), pp. 2281–2286.

  371. 371

    K Ban, S Horikawa, K Taniguchi, T Kogawa, H Kasami, in Proc. IEEE VTC Spring. Digital baseband IC design of OFDM PHY for a 60GHz proximity communication system, (2013).

  372. 372

    TL Marzetta, Noncooperative cellular wireless with unlimited numbers of base station antennas. IEEE Trans. Wireless Commun. 11(9), 3590–3600 (2010).

  373. 373

    E Torkildson, U Madhow, M Rodwell, Indoor millimeter wave mimo: Feasibility and performance. IEEE Trans. Wireless Commun. 10(12), 4150–4160 (2011).

  374. 374

    A Osseiran, et al., Scenarios for 5G mobile and wireless communications: The vision of the METIS project. IEEE Commun. Mag. 52(5), 26–35 (2014).

  375. 375

    A Sabharwal, P Schniter, D Guo, DW Bliss, S Rangarajan, R Wichman, In-band full-duplex wireless: Challenges and opportunities. IEEE J. Selected Areas Commun. 32(9), 1637–1652 (2014).

  376. 376

    G Liu, FR Yu, H Ji, VCM Leung, X Li, In-band full-duplex relaying: A survey, research issues and challenges. IEEE Commun. Surv. Tutorials. 17(2), 500–524 (2015).

  377. 377

    X Lu, P Wang, D Niyato, DI Kim, Z Han, Wireless networks with RF energy harvesting: A contemporary survey. IEEE Commun. Surv. Tutorials. 17(2), 757–789 (2015).

  378. 378

    AA Nasir, X Zhou, S Durrani, RA Kennedy, Relaying protocols for wireless energy harvesting and information processing. IEEE Trans. Wireless Commun. 12(7), 3622–3636 (2013).

  379. 379

    R Zhang, CK Ho, Mimo broadcasting for simultaneous wireless information and power transfer. IEEE Trans. Wireless Commun. 12(5), 1989–2001 (2013).

  380. 380

    H Lin, X Zhu, K Yamashita, Low-complexity pilot-aided compensation for carrier frequency offset and I/Q imbalance. IEEE Trans. Commun. 58(2), 448–452 (2010).

  381. 381

    J Zhang, KC Teh, KH Li, Performance study of fast frequency-hopped/M-ary frequency-shift keying systems with timing and frequency offsets over rician-fading channels with both multitone jamming and partial-band noise jamming. IET Commun.4(10), 1153–1163 (2010).

  382. 382

    HM Kim, SI Park, HM Eum, JH Seo, H Lee, A novel distributed translator for an ATSC terrestrial DTV system. IEEE Trans. Broadcast. 59(3), 412–421 (2013).

  383. 383

    Y Chen, J Zhang, ADS Jayalath, in Proc.IEEE WCNC. Are SC-FDE systems robust to CFO? (2010).

  384. 384

    W-L Chin, ML estimation of timing and frequency offsets using distinctive correlation characteristics of ofdm signals over dispersive fading channels. IEEE Trans. Veh. Technol. 60(2), 444–456 (2011).

  385. 385

    W-L Chin, C-W Kao, H-H Chen, T-L Liao, Iterative synchronization-assisted detection of OFDM signals in cognitive radio systems. IEEE Trans. Veh. Technol. 63(4), 1633–1644 (2014).

  386. 386

    L Yang, G Ren, Z Qiu, A novel doppler frequency offset estimation method for DVB-T system in HST environment. IEEE Trans. Broadcast. 58(1), 139–143 (2012).

  387. 387

    H Zhang, P Wei, Q Mou, A semidefinite relaxation approach to blind despreading of long-code DS-SS signal with carrier frequency offset. IEEE Signal Process. Lett. 20(7), 705–708 (2013).

  388. 388

    L Chen, Y Qiao, Y Zhao, Y Ji, Wide-range frequency offset estimation method for a DD-OFDM-PON downstream system. IEEE/OSA J. Optical Commun. Netw. 4(7), 565–570 (2012).

  389. 389

    H Yang, W Shin, S Lee, Y You, A robust estimation of residual carrier frequency offset with I/Q imbalance in OFDM systems (2014).

  390. 390

    HR Tanhaei, SA Ghorashi, A novel channel estimation technique for OFDM systems with robustness against timing offset. IEEE Trans. Consumer Electron. 57(2), 348–356 (2011).

  391. 391

    L Ge, Y Zhao, H Wu, N Xu, Y Jin, W Li, Joint frequency offset tracking and P