 Research
 Open Access
Optimized spectrum sensing algorithms for cognitive LTE femtocells
 Mahmoud A Abdelmonem^{1}Email author,
 Mohammed Nafie^{1},
 Mahmoud H Ismail^{1} and
 Magdy S ElSoudani^{1}
https://doi.org/10.1186/1687149920126
© Abdelmonem et al; licensee Springer. 2012
 Received: 20 July 2011
 Accepted: 9 January 2012
 Published: 9 January 2012
Abstract
In this article, we investigate to perform spectrum sensing in two stages for a target longterm evolution (LTE) signal where the main objective is enabling coexistence of LTE femtocells with other LTE femto and macrocells. In the first stage, it is required to perform the sensing as fast as possible and with an acceptable performance under different channel conditions. Toward that end, we first propose sensing the whole LTE signal bandwidth using the fast wavelet transform (FWT) algorithm and compare it to the fast Fourier transformbased algorithm in terms of complexity and performance. Then, we use FWT to go even deeper in the LTE signal band to sense at multiples of a resource block resolution. A new algorithm is proposed that provides an intelligent stopping criterion for the FWT sensing to further reduce its complexity. In the second stage, it is required to perform a finer sensing on the vacant channels to reduce the probability of collision with the primary user. Two algorithms have been proposed for this task; one of them uses the OFDM cyclic prefix for LTE signal detection while the other one uses the primary synchronization signal. The two algorithms were compared in terms of both performance and complexity.
Keywords
 Cognitive Radio
 Discrete Wavelet Transform
 Primary User
 Secondary User
 Continuous Wavelet Transform
1. Introduction
Spectrum scarcity has become one of the serious problems facing the wireless communications regulatory bodies especially when the wireless applications and standards are increasing significantly. At the same time, a recent study by the United States Federal Communications Commission (FCC) shows that most of the allocated spectrum in the US is underutilized [1]. Cognitive radio (CR) technology enables other secondary users to coexist with the primary users of a wireless system and to make use of the nonutilized portions of the spectrum, also known as the white spaces, thus making a more efficient utilization of the spectrum [2–4].
One of the most recent wireless standards, where the use of CR is possible, is the longterm evolution (LTE) used for broadband wireless access. LTE could provide data rates up to 100 Mbps in the downlink and 50 Mbps in the uplink in a 20MHz bandwidth; thanks to its powerful physical layer which uses orthogonal frequency division multiple access (OFDMA), multiinput multioutput technology as well as advanced channel coding techniques [5].
Within the context of LTE, CR technology can possibly be used when femtocells are deployed. These are autonomous small cellular base stations designed for use in subscribers' homes and small business environments. They radiate very low power (< 10 mW) and can typically support two to six simultaneous mobile users [6, 7]. Recently, femtocells have attracted strong interest within the telecommunication industry due to the unique benefits they offer, both for the operators as well as the end users. The small, lowcost, and low power home base station improves the indoor coverage and network capacity, increases the average revenue per user, and enhances customers' loyalty [7]. These are very attractive benefits for the operators. As for the end users, the femtocell solution provides better inbuilding call quality and reduced calling cost at home. The battery life is also improved because of the low power radiation [6].
 1
RF interference: femtocells operate in the licensed spectrum owned by mobile operators and they may share the same spectrum with the macrocell network. RF interference could happen between neighboring femtocells, femtocells to macrocells, and vice versa [8]. The spectrum has to be efficiently allocated in the femtocell network to mitigate the interference problem. In [9–12], interference avoidance strategies were developed in a coexisting environment of macrocells and femtocells.
 2
Selfoptimization and autoconfiguration: The femtocell is expected to operate in a plug and play fashion to ease installation, configuration, and management. Methods for selfoptimization and autoconfiguration have been investigated in [13, 14] to optimize the coverage of femtocells and minimize the impact on the macrocell network.
 3
Integration and interoperability with the core network: Femtocells extend the operator's cellular network into homes, providing high data rate services. Thus, integration and interoperability with the operator's existing network and services are important concerns for the operators [14].
The main problem with femotocells deployment is the RF interference that could happen between neighboring femtocells or between femtocells and macrocells. An attractive solution to this problem is to avoid interference by carefully controlling transmission power so as to only just cover the user's home. Yet, this method cannot guarantee interferencefree operation since the femtocell must also provide complete coverage in the user's home. If the user places the femtocell too close to an outside wall or a window, it may not be able to give full coverage while avoiding leakage to a neighbor at the same time. Thus, it could be much better if the LTE femtocell could detect if the frequency band it intends to use is already occupied by another nearby femtocell before starting to operate [15]. A promising solution to this problem is spectrum sensing. It is the responsibility of the new femtocell user, namely, the secondary user, to scan the white spaces in the LTE spectrum and then to transmit in these white spaces, without interfering with the other neighboring LTE users; namely the primary users.
where s(n) is the primary user's signal, u(n) is the noise, which is assumed to be Gaussian independent and identically distributed (i.i.d.) random variables with zero mean and variance σ^{2}. In channel sensing, we are interested in the probability of detection, P_{d}, and the probability of false alarm, P_{f}. P_{d} and P_{f} are defined as the probabilities that a sensing algorithm detects a primary user under hypothesis H_{1} and H_{0}, respectively. There are three important requirements in the sensing process; the first is to keep the probability of detection (P_{d}) of the LTE signal as high as possible, in order to achieve reliable communications for the primary user. The second requirement is to keep the probability of false alarm (P_{f}) as low as possible to achieve efficient radio utilization for the secondary user. Finally, the sensing process and consequently, a correct decision, should be accomplished as fast as possible. A challenging task is to achieve a compromise between the three previously mentioned requirements in order to achieve an acceptable performance in both additive white Gaussian noise channels (AWGN) and fading channels with different Doppler frequencies (f_{d}).
 1.
The first stage is coarse sensing, where we are more concerned with expediting the sensing process while maintaining an acceptable receiver operating characteristic (ROC) in terms of P_{d} and P_{f}. Examples of widely used coarse sensing algorithms are energy detection in the time domain or the frequency domain [17], Waveletbased sensing [18] as well as others.
 2.
The second stage is fine sensing, where another finer stage of sensing is employed in order to double check for the white spaces after the coarse sensing stage to achieve reliable communication for the primary user. Examples of fine sensing algorithms are radio identificationbased sensing [19], cyclostationarity feature detection [20, 21] as well as sensing based on known signal preambles [22, 23].
When designing the spectrum sensing module in a CR system, two important points have to be well considered. The first point is the challenges associated with the spectrum sensing process like the sensing time, which puts a challenge on the CR design as there is a tradeoff between the sensing reliability and the sensing speed [24], the hidden node problem where the CR may not be able to detect the primary transmitter due to shadowing, hence sensing information from other CR users is required for more reliable primary user detection; this is what is called "cooperative sensing" [25]. Finally, the hardware requirements where spectrum sensing for CR applications require operation over wide bands that need wideband RF sections as well as high sampling rate and consequently high resolution analogtodigital converters with large dynamic range and highspeed signal processors [26]. The second point is selecting the most suitable sensing algorithm according to the sensing requirements and the properties of the signal to be sensed. There are various spectrum sensing algorithms in the literature; for example, energy detectorbased sensing [17], waveformbased sensing [27], cyclostationaritybased sensing [20, 21], radio identificationbased sensing [19, 28], and matchedfiltering. When selecting a sensing method, some tradeoffs should be considered. The characteristics of the primary users are the main factors in selecting a method. Cyclostationary features contained in the waveform, existence of regularly transmitted pilots, and timing/frequency characteristics are all important. Other factors include the required accuracy, sensing duration requirements, computational complexity, and network requirements.
In this article, we use CR to solve the interference problem arising from the autonomous deployment of femtocells via reliable and efficient spectrum sensing. In this study, we choose the fast wavelet transform (FWT) algorithm in order to perform the coarse sensing stage and compare its performance against the fast Fourier transform (FFT)based coarse detection in terms of both performance and complexity. The reason behind choosing FWT over other coarse sensing techniques is its ability to decompose the sensing process into a number of stages where a stopping criterion could be applied at a certain stage to reduce the complexity. In particular, a new intelligent decomposition (ID) algorithm is developed, where we provide a stopping criterion for the FWT algorithm based on environmental parameters and predefined thresholds. This algorithm uses a location awareness module to get the wireless channel parameters used for sensing. In addition, a confidence metric was added to indicate the amount of confidence in the decision taken.
The coarse sensing algorithm first scans the whole spectrum to search for the unoccupied LTE channels with the resolution of a complete LTE channel. If none exists, the FWT engine would go further in the LTE spectrum to search with the resolution of a resource block (RB) with a very slight additional complexity; this constitutes another benefit of using FWT over FFT. All this information is then transmitted to the MAC layer that performs the scheduling among the cognitive users.
In the fine sensing stage, two algorithms are proposed; one of them uses the cyclic shift property of the LTE OFDM signal while the other uses one of the LTE synchronization signals, namely, the primary synchronization signal. Fine sensing based on the primary synchronization signal is chosen because it has less complexity as compared to the use of other LTE synchronization signals such as the secondary synchronization signal or the LTE reference signals (pilots), as will be shown later in the sequel. Also, it is shown to perform very well under different wireless LTE channel models. Some optimizations are also done to the cyclic prefix algorithm to enhance its performance and reduce the complexity. Finally, endtoend results are presented showing the performance of both the coarse and fine sensing results collectively for different coarse and fine sensing algorithm pairs under various LTE channel conditions.
The rest of this article is organized as follows: Section 2 explains the LTE coarse sensing stage along with its results while Section 3 explains the fine sensing stage as well as the endtoend system results. Section 4 concludes the study.
2. LTE coarse spectrum sensing
The LTE downlink and uplink transmission schemes are based on OFDMA and single carrier frequency division multiple access (SCFDMA), respectively [29]. The basic LTE scheduling unit in both downlink and uplink is called an RB and consists of 12 subcarriers with a spacing of 15 kHz (corresponding to 180 kHz overall) in the frequency domain and six or seven consecutive OFDM symbols (SCFDMA symbols for the uplink) in the time domain. The number of available RBs in the frequency domain varies depending on the channel bandwidth, which increases from 6 to 100 when the bandwidth changes from 1.4 to 20 MHz, respectively. In the time domain, each RB spans a slot, with a duration equivalent to six or seven symbols (0.5 ms). Two slots correspond to one subframe and ten subframes typically form a frame (10 ms). LTE supports both time division duplexing (TDD) and frequency division duplexing (FDD). For TDD, a subframe within a frame can be allocated to downlink or uplink transmissions. In the case of FDD, because the downlink and uplink transmissions are separated in the frequency domain, there is no allocation of subframes in time.
In this section, we are mainly concerned with the coarse sensing part of the LTE spectrum sensing module. First, we give a brief summary on wavelets in general explaining the FWT algorithm to be used for sensing. After that, we move to a novel proposed algorithm that uses the wavelet packet transform algorithm to perform the coarse sensing stage assuming that the primary signal is an LTE signal.
2.1 Fast wavelet transform
A wavelet is a waveform of effectively limited duration that has an average value of zero. Comparing sine waves which are the basis of Fourier analysis with wavelets, sinusoids do not have limited duration. In addition, sinusoids are smooth and predictable while wavelets tend to be irregular and asymmetric [30].
 1.
Start with a wavelet and compare it to a section at the start of the signal.
 2.
Calculate a number, C, which represents how much correlation exists between the wavelet and this section of the signal, the higher C is, the more the similarity.
 3.
Shift the wavelet to the right and repeat steps 1 and 2 till the end of the signal.
 4.
Scale (stretch) the wavelet and repeat steps 1 through 3.
 5.
Repeat steps 1 through 4 for all scales.
Higher scales correspond to more stretched wavelets. The more stretched the wavelet, the longer the portion of the signal with which it is being compared, and thus the coarser the signal features being measured by the wavelet coefficients. Similarly, lower scales correspond to more compressed wavelets and thus measuring the finer signal details [30].
The CWT can operate at every scale, from that of the original signal up to some maximum scale that is determined by trading off the need for detailed analysis with available computational power. On the other hand, discrete wavelet transform (DWT) operates on discrete levels of scale.
The FWT is a computationally efficient implementation of the DWT that exploits the relationship between the DWT coefficients at adjacent scales [30]. In wavelet analysis, we often speak of approximations and details. The approximations are the highscale, lowfrequency components of the signal. The details are the lowscale, highfrequency components. In an FWT filtering process, a signal is split into an approximation and a detail. The approximation is then itself split into a secondlevel approximation and detail, and the process is repeated.
2.2 FWT LTE sensing performance versus FFT
LTE system parameters used in the spectrum sensing model
LTE system parameters  

Duplex mode  FDD 
FFT size  2048 
Number of RBs  25 
Number of carriers per RB  12 
Number of useful carriers  300 
Subcarrier spacing  15 kHz 
LTE channel BW  4.5 MHz 
Modulation per subcarrier  QPSK 
Number of LTE channels  8 
System sampling frequency  80 MHz 
FWT versus FFT sensing complexity comparison
FWT  FFT 

A single FWT operation per LTE OFDM symbol (5 slots × 7 FWT operations)  The five LTE slots are divided into FFT blocks according to the FFT size, the average FFT of these blocks is the output of the FFT sensing module 
Complexity = 2 × (Number of samples per LTE OFDM Symbol) × 7 × 5 × M × L  Complexity = (Number of FFT blocks per 5 LTE slots) × FFT_Size × log_{2}(FFT_Size) 
Daubechies (dbN) wavelets are used where N is the filter order  256 and 512 point FFT modules are used 
1598520 computations for db2 FWT 3197040 computations for db4 FWT  1599488 computations for 256point FFT 1797120 computations for 512point FFT 
2.3 RB resolution sensing algorithm
 1.
Resample the LTE signal to extend the visible BW to 5.76 MHz, where the number of RBs becomes 32 which is an integer power of 2 in order to be capable of applying the FWT algorithm.
 2.
Shift the signal spectrum by the amount equal to the guard band to align the spectrum to its edge.
 3.
Apply a 5stage FWT sensing till we reach the RB resolution.
 1.
The first one is that since the sensing resolution is increased to an RB (i.e., 180 kHz), we will need to perform five FWT stages so the signal is downsampled five times leaving a small number of samples per LTE RB to be used for detection. A solution might be increasing the number of the input signal samples which means increasing the sensing time. Since it is required to perform fast sensing in the coarse stage, the resolution in our simulations is reduced to four RBs instead of one to avoid this problem.
 2.The second issue is related to the transmission of the pilot signals in OFDM symbols number 0 and 4 within the slot on a oneoutofsix basis (i.e., each RB has two pilots in these symbols) as shown in [29], where the output will be higher than normal due to the additional pilot energy. This has two possible solutions:
 i.
Properly choosing the decision threshold to mitigate the higher energy due to pilots.
 ii.
During transmission there is a need for a cooperating LTE base station to transmit zeros in nonassigned RBs.
 i.
2.4 ID algorithm
Since the complexity of the sensing algorithm is one of our main concerns, a new algorithm is now proposed to further reduce the FWT complexity. This is a generic algorithm that could be applied in case the sensing resolution is the whole LTE channel or multiples of an RB as described in the previous section.
 1
The approximation and detail after every FWT decomposition stage shall be denoted by the name section. So, first of all, the power of each section is computed.
 2
Then the number of channels per section in this stage is computed as (Total Number of LTE Channels)/2^{(Decomposition Stage)}. and then used to get the power per LTE channel.
 3
It is assumed that there exists another location awareness module not implemented here, this module provides us with some important parameters like:
 A.Largescale environmental parameters:

Average LTE signal power, which depends on the distance from the transmitter and the transmitted power. In case of femtocells, this parameter will be different from the case of a macro cell.

Shadowing margin, which depends on the environment whether it is urban, suburban, or a rural area.

 B.
Small scale environmental parameters such as the fading margin that depends on the wireless channel between the femtocell and the user, this parameter also varies depending on whether we are considering femto or macro cells.
 C.Sensing parameters:

Positive margin: Used to calculate the upper threshold value above which the section is considered to be occupied.

Negative margin: Used to calculate the lower threshold value below which the section is considered to be vacant, this value should be more conservative than the positive threshold as it will decide for this section and its channels to be vacant.

 4Then the upper and lower thresholds are computed as follows:

Upper threshold = Average power + Fading margin + Positive sensing margin

Lower threshold = Average power  Fading margin  Negative sensing margin  Shadowing margin

 5These thresholds are used to decide for the channel state:

If Power > Upper threshold, the section state is considered occupied, thus no further wavelet filtering is applied as the LTE channels in this section will be considered occupied.

If Power < Lower threshold, the section state is considered vacant thus no further wavelet filtering is applied and the LTE channels in this section will be considered vacant.

Otherwise, the section state is considered normal so we shall continue applying wavelet filtering as in the normal case.

 6
The declared "state" is used to fill a "state matrix" upon which we make our decision to apply wavelet filtering or not as described above. The state matrix has two dimensions: section and decomposition stage as shown in Figure 9. The section dimension (horizontal) represents the part of the LTE spectrum being sensed, while the decomposition stage dimension (vertical) represents the FWT current decomposition stage.

The channel is an AWGN channel thus the fading and shadowing margins equal to zero.

The average power received from the base station is known.

The application using the algorithm and how much sensitive it is to the sensing false alarm rate that leads to some waste of bandwidth.

The application of the primary user and how much sensitive it is to a missed detection by the cognitive user that consequently affects the primary user QOS.

The hardware requirements and power consumption requirements of the sensing module.
It also has to be taken into consideration that deciding for the whole section to be vacant is a critical decision as this means that all of its channels will be considered vacant as well, thus the secondary user can use them after passing the fine sensing stage. That is why the negative sensing threshold should be more conservative than the positive one as it will affect the lower threshold below which the section is considered vacant. This algorithm shows a clear advantage of FWT over FFT as it could not be applied on FFT.
A further enhancement to the ID algorithm is now in order. It is possible to compute a weighted average of the channel states to take the final decision. This weight is a function of the difference between the channel power and the predefined threshold. In case the channel power is far below or above the threshold, a higher weight is given to the corresponding state which is vacant or occupied, respectively.

Confidence Metric Algorithm 1 uses the difference between the channel power and the predefined threshold,

Confidence Metric Algorithm 2 uses the square of the difference between the channel power and the predefined threshold.

For higher P_{d}, the confidence metric algorithm gives better results. In case of spectrum sensing, higher P_{d} is more important than lower P_{f}, as in case of a missed detection this will lead to collision with the primary user, which is unacceptable for CR systems.

In case of lower probability of false alarm, using confidence metric algorithm gives a worse performance than the normal algorithm. This observation may vary according to the values of the chosen thresholds. In case we choose different threshold values, we could end up with the algorithm being better in case of lower probability of false alarm. The optimal calculation of the thresholds is out of scope of this study and could be added in the future study.

In general, using algorithm 1 is better than algorithm 2 where using the square of the difference enlarges the large differences and reduces the small differences, which might lead to false decisions as compared to using the difference alone without squaring.
A global comparison between FWT and FFT coarse sensing methods
FWT  FFT 

Better at obtaining a higher P_{d} which is important to satisfy the required primary user QoS. Used when the primary user QoS is of higher concern  Better at obtaining a lower P_{f} which is important to achieve better spectral efficiency. Used when the spectral efficiency is of higher concern 
The sensing resolution could be simply increased to reach RB resolution by applying further FWT decompositions  To increase the sensing resolution we need to increase the FFT size 
An ID algorithm could be applied at each wavelet decomposition stage to reduce the number of FWT operations with an acceptable performance  The ID algorithm is not applicable to FFT where the operation is performed in one stage 
In case the LTE receiver is an SDR and has a programmable FFT core, we lose the option of reusing this core which is used in the LTE OFDM receiver to perform spectrum sensing  When the receiver is an SDR with a programmable FFT core, we can simply reuse the same FFT core used in the LTE OFDM receiver to perform spectrum sensing thus reducing complexity 
3. LTE fine spectrum sensing
Referring to the main system flow chart in Figure 4, we have shown that the coarse sensing module mainly concentrates on quick detection of empty spaces to be used by the CR user. But in order to have a more reliable detection for the empty spaces, we need to perform fine sensing on them. In this section, two fine sensing algorithms are proposed; one of them uses the cyclic shift property of the LTE OFDM signal while the other one uses one of the LTE synchronization signals. A detailed explanation is given for the two proposed fine sensing algorithms along with their results and enhancements. Finally, the endtoend system results are shown in case of different coarse and fine sensing module pairs.
3.1 Cyclic prefix correlation sensing
3.1.1 Normal CP algorithm
In this algorithm, CP correlation using a sliding window is performed over a number of OFDM symbols. The peak indices are then investigated and the decision for LTE signal existence is based on a majority vote for the number of peaks. The normal cyclic prefix configuration is assumed where the first OFDM symbol in the slot has a CP composed of 160 samples compared to 144 samples for the remaining 6 OFDM symbols.

Input signal is X(n)

The correlator output is Y(n)

The correlation window size is 160 which is the maximum CP length. The FFT size is denoted by the symbol N_{FFT}. It is important to note here that if the window size is taken to be 144, the algorithm will be suboptimum in case of the first OFDM symbol in the slot because the first symbol has a CP of length 160 samples, while for the other symbols, the CP length is 144 samples. In that case, we are not making use of the whole 160 samples in the CP of this symbol. For the remaining symbols, the correlation will be optimum in case of a 144 length window because we shall use the whole 144 CP samples in the correlation.
3.1.2 CP algorithm with folding
3.2 Primary sync correlation sensing
In LTE, there are three known signals transmitted in the downlink: the Primary synchronization signal (PSCH), the Secondary synchronization signal (SSCH), and the reference signals (Pilots). Our main target in this section is to design an algorithm that detects the LTE signal reliably and with the least possible complexity using the above mentioned known signals. We can simply correlate the received signal with a replica from the synchronization signals and compare the correlation peak against a certain threshold to indicate the existence of an LTE signal. The question now is which one of the above three signals could be used. As for the PSCH, although it is generated as an OFDM signal, it could be entirely detected in the timedomain with no need for an FFT operation. The SSCH, however, is typically detected in the frequency domain. Moreover, in LTE, there are 504 cell IDs which are divided into 168 group IDs, where each group contains three identities. The 168 groups are encoded into the SSCH whereas the PSCH signal index determines the identity within the group [32].
 1
Only three correlations need to be carried out instead of 168 correlations if SSCH is used.
 2
Detection could be performed in the time domain with no need for FFT processing before correlation.
Using the LTE Reference signals (pilots) for fine sensing will be very difficult as it requires the knowledge of the slot and symbol index in addition to the whole cell ID. That is why the PSCH is chosen to perform the fine sensing algorithm for LTE.
Numerous investigations were done in 3GPP for the selection of the sequence indices u. It was concluded that the sensitivity to large frequency offsets was smallest when the indices are selected close to half the sequence length. The sequence indices have been chosen as u = 25, 29, and 34. Also it can easily be proved that the signal obtained from u = 29 is a complex conjugated version of u = 34, this property will lead to a reduction in the matched filter complexity as the two corresponding matched filters can be implemented with the multiplication complexity of just one filter as shown below:
We can see from the above equations [33] that the difference lies only in the signs and that we can perform the multiplications only once. The PSCH signal is also centrally symmetric, which means that the number of multiplications in the corresponding matched filter could be reduced. There are 62 centrally symmetric samples of the PSCH signal. These sample pairs can be added prior to multiplication, so the matched filter can be implemented by almost half the multiplications required in the direct implementation.
 1
Minimizing the number of multiplications by half for each matched filter through addition of symmetric samples.
 2
Making it possible to detect the three PSCH signals with a multiplication complexity corresponding to only two matched filters.
3.3 Endtoend system results
4. Conclusions
In this article, spectrum sensing is performed for an LTE signal in two stages; a coarse stage and a fine stage. An algorithm is proposed that uses the wavelet packet transform algorithm to perform the coarse sensing stage assuming that the primary signal is an LTE signal. The challenges associated with the proposed algorithm are mentioned as well as a comparison with FFTbased coarse detection in terms of both performance and complexity has been introduced. The comparison shows that FWT and FFT have almost the same performance. Simulations have shown that reducing the sensing resolution of the FWT algorithm to an RB requires sharp filters and is impractical, that is why sensing is done at multiples of an RB. Also, a new ID algorithm has led to a further reduction in the FWT complexity where we provide a stopping criterion for the normal FWT algorithm based on environmental parameters and predefined thresholds, this provides FWT sensing with an advantage over FFT sensing as the algorithm is not applicable to FFT. The results of this algorithm have shown that a compromise has to be made between the FWT complexity and the required probability of detection and false alarm. Optimally setting the thresholds of this algorithm is a subject of future research. A confidence metric has been added to the ID algorithm which mainly applies a weighted average of the sensed channel states to arrive at the final decision. This weight is a function of the difference between the channel power and the predefined threshold. The confidence metric algorithm outperforms the normal one in case high P_{d} is required, which is the most important parameter in case of spectrum sensing for CR systems.
In the fine sensing stage, two algorithms are proposed. The first algorithm is the CP correlation sensing. An iterative structure with fewer multiplications is compared versus the normal structure in terms of complexity where both algorithms provide the same performance. Also, simulations results have shown that using folding in CP correlation reduces the correlation buffer size and increases the sensing gain especially in multipath channels. The second proposed fine sensing algorithm requires one of the known LTE synchronization signals, we have shown that using the PSCH is the most suitable as the SSCH and pilots require far more complexity. The PSCH correlation algorithm was proved to be more reliable than the CP correlation algorithms in different LTE channel models. Finally, the endtoend system results show the gain obtained in case of using the fine sensing module after the coarse one versus using the coarse module alone for different coarse and fine module pairs.
Declarations
Acknowledgements
This article has been presented in part at the 17th IEEE International Conference on Telecommunications (ICT) 2010, Doha, Qatar, April 2010.
Authors’ Affiliations
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