- Research
- Open Access
A new reduced-complexity detection scheme for zero-padded OFDM transmissions
- Homa Eghbali^{1} and
- Sami Muhaidat^{1}Email author
https://doi.org/10.1186/1687-1499-2011-109
© Eghbali and Muhaidat; licensee Springer. 2011
- Received: 19 December 2010
- Accepted: 25 September 2011
- Published: 25 September 2011
Abstract
Recently, zero-padding orthogonal frequency division multiplexing (ZP-OFDM) has been proposed as an alternative solution to the traditional cyclic prefix (CP)-OFDM, to ensure symbol recovery regardless of channels nulls. Various ZP-OFDM receivers have been proposed in the literature, trading off performance with complexity. In this paper, we propose a novel low-complexity (LC) receiver for ZP-OFDM transmissions and derive an upper bound on the bit error rate (BER) performance of the LC-ZP-OFDM receiver. We further demonstrate that the LC-ZP-OFDM receiver brings a significant complexity reduction in the receiver design, while outperforming conventional minimum mean-square error (MMSE)-ZP-OFDM, supported by simulation results. A modified (M)-ZP-OFDM receiver, which requires the channel state information (CSI) knowledge at the transmitter side, is presented. We show that the M-ZP-OFDM receiver outperforms the conventional MMSE-ZP-OFDM when either perfect or partial CSI (i.e., limited CSI) is available at the transmitter side.
Index Terms-Zero-padding, orthogonal frequency division multiplexing (OFDM), equalization.
Keywords
- Orthogonal Frequency Division Multiplex
- Channel State Information
- Finite Impulse Response
- Orthogonal Frequency Division Multiplex System
- Cyclic Prefix
I. Introduction
The growing demand for high data rate services for wireless multimedia and internet services has led to intensive research efforts on high-speed data transmission. A key challenge for high-speed broadband applications is the dispersive nature of frequency-selective fading channels, which causes the so-called intersymbol interference (ISI), leading to an inevitable performance degradation. An efficient approach to mitigate ISI is the use of orthogonal frequency division multiplexing (OFDM) that converts the ISI channel with additive white Gaussian noise (AWGN) into parallel ISI-free subchannels by implementing inverse fast Fourier transform (IFFT) at the transmitter and fast Fourier transform (FFT) at the receiver side [1]. It has been shown that OFDM is an attractive equalization scheme for digital audio/video broadcasting (DAB/DVB) [2, 3] and has successfully been applied to high-speed modems over digital subscriber lines (DSL) [4]. Recently, it has also been proposed for broadband television systems and mobile wireless local area networks such as IEEE802.11a and HIPERLAN/2 (HL2) standards [5].
The IFFT precoding at the transmitter side and insertion of CP enable OFDM with very simple equalization of frequency-selective finite impulse response (FIR) channels. To avoid in-terblock interference (IBI) between successive FFT processed blocks, the CP is discarded and the truncated blocks are FFT processed so that the frequency-selective channels are converted into parallel flat-faded independent subchannels. In this way, the linear channel convolution is converted into circular convolution, and the receiver complexity both in equalization and the symbol decoding stages is reduced [6]. However, since each symbol is transmitted over a single flat subchannel, the multipath diversity is lost along with the fact there is no guarantee for symbol detectability when channel nulls occur in the subchannels. As an additional counter-measure, coded OFDM (C-OFDM), which is fading resilient, has been adopted in many standards [7]. Trellis-coded modulation (TCM) [8] and convolutional codes [9, 10] are typical choices for error-control codes. Interleaving together with TCM enjoys low-complexity Viterbi decoding while enabling a better trade off between bandwidth and efficiency. However, designing systems that achieve diversity gain equal to code length is difficult considering the standard design paradigms for TCM. Linear constellation precoded OFDM (LCP-OFDM) was further proposed in [11] to improve performance over fading channels, where a real orthogonal precoder is applied to maximize the minimum product distance [12, 13] and channel cutoff rate [14], while maintaining the transmission rate and guaranteeing the symbol recovery. In [15, 16], the LCP is combined with space-time block coding (STBC) and channel coding in order to effectively improve the space-time diversity order of the multiple antenna system. The aforementioned LCP systems are based on Hadamard matrices and can employ the simple MMSE linear decoder at the receiver side, which has low complexity compared to the maximum likelihood (ML) decoders currently used in such systems. Subcarrier grouping was proposed in [11] to enable the maximum possible diversity and coding gain. However, the LCP that is used within each subset of subcarriers is in general complex.
To ensure symbol recovery regardless of channel nulls, Zero-Padding (ZP) of multicarrier transmission has been proposed to replace the generally non-zero CP. Unlike CP-OFDM, ZP-OFDM guarantees symbol recovery and FIR equalization of FIR channels. Specifically, the zero symbols are appended after the IFFT processed information symbols. In such way, the single FFT required by CP-OFDM is replaced by FIR filtering in ZP-OFDM that adds to the receiver complexity. To trade-off BER performance for extra savings in complexity, two low-complexity equalizers for ZP-OFDM are derived in [17]. Both schemes are developed based on the circularity of channel matrix while reducing complexity by avoiding inversion of a channel dependent matrix. However, the BER performance of the two sub-optimal receivers proposed in [17] is not as good as MMSE-ZP-OFDM.
Adapting the transmitter designs to the propagation channel facilitates performance improvement. Depending on the CSI available to the transmitter, various parameters such as power levels, constellation size, and modulation are adjustable by the channel-adaptive transmissions [18]-[20]. The challenge in a wireless setting is on whether and what type of CSI can be practically made available to the transmitter, where fading channels are randomly varying. Apparently, this is less of an issue for discrete multi-tone (DMT) systems in wireline links, which has been standardized for digital subscriber line modems. In both single-input single-output (SISO) and multiple-input multiple-output (MIMO) versions of DMT, perfect CSI is assumed to be available at the transmitter [21]. Although it is reasonable for wireline links, we can only justify the assumption of perfect CSI-based adaptive transmissions developed for SISO [22] and MIMO OFDM wireless systems [23] when the fading is sufficiently slow. Since no-CSI leads to robust but rather pessimistic designs and perfect CSI is mostly impractical for wireless links, recent efforts geared toward exploitation of partial CSI-based transmissions. In this work, we assume limited CSI knowledge. In particular, we assume that only small number of channel taps are available at the transmitter.
In this paper, we propose a novel reduced-complexity receiver design for ZP-OFDM transmissions (LC-ZP-OFDM) that not only outperforms MMSE-ZP-OFDM (confirmed through simulation results) but also uses low-complexity computational methods that bring a significant power saving to the proposed receiver. We show that linear processing collects both multipath and spatial diversity gains, leading to significant improvement in performance, while maintaining low complexity. Note that the proposed LC-ZP-OFDM receiver consists of two stages, i.e., a low-complexity sub-optimal MMSE receiver at first stage followed by linear operations at second stage to investigate the underlying available diversity. A M-ZP-OFDM receiver for transmitter designs based on perfect, as well as limited CSI knowledge, is further included, which significantly outperforms MMSE-ZP-OFDM. BER upper bounds are presented for the two-stage LC receiver. The performance improvement of the LC-ZP-OFDM and M-ZP-OFDM receivers as compared with the conventional MMSE-ZP-OFDM receiver is further corroborated through simulation results.
The rest of this correspondence is organized as follows. We start in Section II by describing our model and assumptions. In Section III, the reduced-complexity MMSE-based receiver is proposed. Computational complexity analysis and numerical results are presented in Sections IV and V, respectively. The paper is concluded in Section VI.
II. System model
We consider a single-transmit single-receive antenna system over a frequency-selective fading wireless channel. The channel impulse response (CIR) of the j th information block is modeled as an FIR filter with coefficients h^{ j }= [h^{ j }[0],..., h^{ j }[L]]^{T}, where L denotes the corresponding channel memory length. The random vectors h^{ j } are assumed to be independent zero-mean complex Gaussian with two choices of power-delay profile: Uniform power-delay profile with variance 1/(L + 1), and exponentially decaying power-delay profile $\theta \left({\tau}_{k}\right)=C{e}^{-{\tau}_{k}\u2215{\tau}_{rms}}$ with delays τ_{ k } that are uniformly and independently distributed [24].
It should be emphasized that channel inversion of a N × N matrix cannot be precomputed due to its dependence on channel that brings about an extra implementation cost for the MMSE-ZP-OFDM receiver. Specifically, equalization in (5) is computationally costly, as the pseudo inverse of the Toeplitz matrix H_{0} is required, whose precomputation is impossible due to the channel coefficients' variations from block to block. Thus, from an implementation point of view, a low-complexity ZP-OFDM is desirable. This observation motivates the subsequent sub-optimal low-complexity ZP-OFDM equalizers, such as ZP-OFDM-FAST-ZF/MMSE and the ZP-OFDM-OLA [17] originating from the overlap-and-add (OLA) method. Thus, complexity is always an issue when dealing with ZP-OFDM systems, as compared to the computationally efficient FFT-based CP-OFDM systems. Its worth mentioning that ZP-OFDM-OLA is analogous to CP-OFDM; thus, it incurs identical complexity and is not able to fully exploit the underlying multipath diversity. To overcome these limitations, our proposed scheme targets practical ZP-OFDM receiver that exploits full multipath diversity while maintaining low complexity.
III. Reduced-complexity mmse-based receiver
The signal in (16) is then fed into a maximum likelihood decoder to recover the transmitted symbols. It is observed from (16) that the maximum achievable diversity order is given by L + 1. This illustrates that our proposed receiver is able to fully exploit the underlying spatial and multipath diversity gains relying on simple linear processing operations only.
IV. M-ZP-OFDM RECEIVER
Note that for l = 1,..., L + 1, both C_{1,l}and Ψ_{1,l}have unit power and thus do not lead to noise enhancement. More importantly, it can be seen that (20) is similar to (14) in LC-ZP-OFDM proposed in Section III, in the sense that we are exploiting the underlying multipath diversity from each diversity branch.
V. Analytical bounds on the ber performance for the low-complexity receiver
Minimum squared distance for different modulation types with average power normalized to one
Modulation | ${d}_{c}^{2}$ |
---|---|
BPSK | 4 |
QPSK | 2 |
16-PSK | 0.1522 |
64-PSK | 0.0096 |
16-QAM | 0.4 |
64-QAM | 0.0952 |
where $\left(\begin{array}{c}\hfill a\hfill \\ \hfill b\hfill \end{array}\right)$ is the binomial coefficient, and $SNR=\frac{{\sigma}_{d}^{2}}{{\sigma}_{n}^{2}}$.
where $SN{R}^{\prime}=\frac{M}{M+N}SNR$. Therefore, (22) can be reconstructed following (25, 26), and (28).
VI. Computational complexity analysis
In this section, we focus on the computational complexity of the proposed receiver compared to the conventional MMSE-ZP-OFDM receiver. The computational complexity is measured based on the number of the complex multiplications and complex divisions. Since complex divisions can be implemented in various different ways, we did not decompose them into complex multiplication. Note that it is not trivial to elaborate explicit complexity expressions for different OFDM systems in terms of the system's parameters. Specifically, system's complexity depends on the parameters such as N, M, D, and L, each of which are decisive in finding the number of the complex multiplications involved. Finding a closed-form expression in terms of the aforementioned system parameters is rather difficult; therefore, in this work, we have derived expressions for the approximate multiplicative complexity counts and have used Matlab simulations to find the exact number of complex multiplications implemented. To do so, we have used the minimal multiplicative bounds [26] to find the arithmetic complexity of FFT of different block lengths. Besides, to implement the FFT of length W decomposable to the product of two prime numbers a and b, we have implemented a FFTs of size b or b FFTs of size a instead, without inquiring any additional operations such as multiplications by twiddle factors [27].
where [·] stands for the approximate operation count involved in the operation implementation. (28) requires total of N^{3} + N^{2} (N + M + 2) + M^{2} + M (N + 1) operations.
Therefore, the total number of operations required to implement the low-complexity receiver is N ((M + 6) L + M) + M (L + 1).
which shows the additional complexity cost at the receiver side. Therefore, the total number of operations required to implement the low-complexity receiver is N (NL + L + M) + M.
Comparison of overall computational complexity
Implementation | Number of complex multiplications | Number of complex divisions | ||
---|---|---|---|---|
Scenario 1 | Scenario 2 | Scenario 1 | Scenario 2 | |
ZP-OFDM-MMSE | 16,372 | 17,087 | NONE | NONE |
LC-ZP-OFDM | 2,688 | 8,408 | 80 | 80 |
M-ZP-OFDM | RC = 3,3408 | RC = 66,648 | NONE | NONE |
TC = 25,600 | TC = 51,200 |
Scenario (1) L + 1 = 5
Scenario (2) L + 1 = 10
It is observed from Figure 1 that for scenarios one and two with reasonable memory length for the underlying channels, the aggregate complexity of the LC-ZP-OFDM receiver is insignificant compared to the conventional MMSE-ZP-OFDM as well as M-ZP-OFDM, as channel parameters change with time.
VII. Numerical results
In this section, we present Monte-Carlo simulation results for the proposed receiver assuming a quasi-static Rayleigh fading channel.
VIII. Conclusion
We propose a novel reduced-complexity MMSE-based receiver for ZP-OFDM systems. We show that, by incorporating linear processing techniques, our MMSE-based receiver is able to collect full antenna and multipath diversity gains. Simulation results demonstrate that our LC-ZP-OFDM receiver outperforms the conventional MMSE-ZP-OFDM receiver, while maintaining much less complexity by avoiding channel dependent matrix inversion. By using linear processing techniques that require minimum computational complexity, the communication power is minimized at no additional hardware cost. We also propose a modified receiver that outperforms the conventional MMSE-ZP-OFDM.
The authors declare that they have no competing interests.
IX. Endnotes
Notation: $\overline{\left(.\right)}$, (.)^{T}, (.)^{†} and (.)^{H} denote conjugate, transpose, pseudo inverse, and Hermitian transpose operations, respectively. |.| denotes the absolute value, and ||.|| denotes the Euclidean norm of a vector. [.]_{k,l}denotes the (k, l)th entry of a matrix, [.]_{ k } denotes the kth entry of a vector, I_{ M } denotes the identity matrix of size M, and 0_{M×M}denotes all-zero matrix of size M × M. Q represents the M × M FFT matrix whose (l, k) element is given by $Q\left(l,k\right)=1/\sqrt{M}exp\left(j2\pi lk/M\right)$ and ${Q}_{M}^{-1}={Q}_{M}^{H}$ represents the M × M IFFT matrix where 0 ≤ l, k ≤ M - 1. Bold upper-case letters denote matrices and bold lower-case letters denote vectors.
Declarations
Authors’ Affiliations
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