Performance limits of conventional and widely linear DFTprecodedOFDM receivers in wideband frequencyselective channels
 Kiran Kuchi^{1}Email author
https://doi.org/10.1186/168714992014159
© Kuchi; licensee Springer. 2014
Received: 13 March 2014
Accepted: 26 August 2014
Published: 3 October 2014
Abstract
This paper describes the limiting behavior of linear and decision feedback equalizers (DFEs) in single/multiple antenna systems employing real/complexvalued modulation alphabets. The wideband frequencyselective channel is modeled using a Rayleigh fading channel model with infinite number of time domain channel taps. Using this model, we show that the considered equalizers offer a fixed post detection signaltonoise ratio (postSNR) at the equalizer output that is close to the matched filter bound (MFB). General expressions for the postSNR are obtained for zeroforcing (ZF)based conventional receivers as well as for the case of receivers employing widely linear (WL) processing. Simulation is used to study the bit error rate (BER) performance of both minimummeansquareerror (MMSE) and ZFbased receivers. Results show that the considered receivers advantageously exploit the rich frequencyselective channel to mitigate both fading and intersymbol interference (ISI) while offering a performance comparable to the MFB.
1 Introduction
Linear and decision feedback equalizers (DFEs) have been widely studied for the past 50 years. With the introduction of discrete Fourier transformprecodedorthogonal frequencydivision multiple access (DFTprecodedOFDMA) [1, 2] in the uplink of the longterm evolution (LTE) standard [3], there has been renewed interest in the design and analysis of these two receivers operating in wideband frequencyselective channels. DFTprecodedOFDM, also known as singlecarrier FDMA (SCFDMA), is a variant of OFDM in which the modulation data is precoded using the DFT before mapping the data on the subcarriers. The resultant modulation signal exhibits low peaktoaverage power ratio (PAPR). As the frequencyselective channel introduces intersymbol interference (ISI), this method requires sophisticated channel equalization at the receiver.
In broadband wireless systems employing high bandwidths, the propagation channel typically exhibits high frequency selectivity. For these systems, link performance measures such as the diversity order and bit error rate (BER) of a conventional minimum meansquare error (MMSE)based linear equalizers have not yet been fully characterized [4–9]. The noise enhancement phenomenon which is inherent in linear equalizers poses a difficulty in analyzing the receiver performance. The minimum meansquare error decision feedback equalizer (MMSEDFE) [10, 11], on the other hand, is an optimum canonical receiver for channels with ISI. In frequencyselective channels, it provides full diversity, and the performance is generally comparable to the optimum matched filter bound (MFB) [12]. Most of the prior works related to linear and decision feedback equalizers discuss the diversity order of the equalizers and do not quantify the exact performance of the equalizer. In many cases, simulation is typically used to determine the link performance.
The performance loss caused by the decision feedback section of the MMSEDFE can be minimized by using a receiver structure that uses the MMSEDFE feedforward filter (FFF) as a prefilter [13] which provides a minimum phase response followed by a reduced state sequence estimation (RSSE) [14] algorithm that uses set partitioning and state dependent decision feedback principles. Note that the maximum likelihood sequence estimator (MLSE) [15, 16] can be viewed as a special case of RSSE. In typical channels, RSSE with an appropriately chosen number of states performs close to MLSE [17]. In spite of the availability of a number of alternatives to MLSE, linear and decision feedback equalizers are generally preferred in wideband systems due to low implementation complexity.
In DFTprecodedOFDM systems, the MMSEDFE [18–20] equalizer can be implemented efficiently using a frequency domain FFF followed by a time domain DFE [21–31]. Computation of FFF and feedback filters (FBF) for DFTprecodedOFDM differs from conventional singlecarrier methods. Since DFTprecodedOFDM permits frequency domain equalization, it simplifies the computational requirements of both filter calculation and implementation. In [32], an iterative block DFE method is proposed. This method uses a linear equalizer in the first iteration and applies blocklevel soft decision feedback in subsequent iterations. In this paper, we are mainly concerned with the analysis of conventional DFEs based on hard decision feedback.
For realvalued data transmission (e.g., binary phaseshift keying (BPSK) or amplitudeshift keying (ASK)), widely linear (WL) equalizers which jointly filter the received signal and its complexconjugate [33] are known to outperform conventional receivers. This concept has been applied for numerous wireless applications [34–43] including equalization, interference suppression, multiuser detection, etc. Implementation WL equalizers is discussed in [39] for conventional time domain singlecarrier systems. WL receiver algorithms are widely employed in global system for mobile communication (GSM) for (a) lowcomplexity equalization of binary Gaussian minimum shift keying (GMSK) modulation in frequencyselective channels (b) cochannel interference suppression using a singlereceiver antenna. The latter feature is popularly known as single antenna interference cancelation (SAIC) [43, 44].
Throughout this paper, we assume that the receiver has multiple spatially separated antennas. However, the analysis, and the results of this paper hold for the case of single antenna as well. We consider a channel with v time domain taps where the individual taps are modeled as independent and identically distributed (i.i.d.) complex Gaussian random variables with zero mean with per tap variance of $\frac{1}{v}$. The postprocessing signaltonoise power ratio (postSNR) of the considered equalizers is analyzed in the limiting case as v → ∞. Using this model, Kuchi [45] has shown that the SNR at the output of a multiantenna zeroforcing linear equalizer (ZFLE) with N_{ r } antennas reaches a mean value of $\frac{{N}_{r}1}{{\sigma}_{n}^{2}}$, where ${\sigma}_{n}^{2}$ denotes the noise variance and N_{ r }> 1. For the case of the singlereceiver antenna, both ZFLE and MMSELE are shown to perform poorly. Therefore, it is worthwhile to consider the DFE as an implementation alternative.
In this paper, we further generalize the results of [45] and analyze the limiting performance of three receiver algorithms, namely (a) conventional ZFDFE, (b) WL ZFLE and (c) WL ZFDFE. While ZFbased methods permit analytical evaluation of the postSNR of the receiver, simulation is used to study the performance of MMSEbased receivers. The postSNR bounds developed in this paper provide new insights into the receiver performance. Specifically, we show that, in i.i.d. fading channels with infinitely high frequency selectivity, the postSNR at the output of all the considered receivers reach a fixed SNR. Using these results, we quantify the performance gap of a given receiver with respect to the MFB. In contrast to the previous works where the focus is restricted to diversity analysis, the results of this paper provide a framework to analyze the link performance in channels with high frequency selectivity.
We would like to remark here that in multiuser OFDMA systems, impairments such as frequency offsets, I/Q imbalance, and channel time variations affect the orthogonality of subcarriers and give rise to multiuser interference. Sophisticated equalization techniques are proposed in [46–49] to combat these impairments. In this paper, we restrict our attention to performance analysis in the presence of frequencyselective channels without considering any of the aforementioned impairments.
The organization of the paper is as follows: In section 3, we first generalize the finitelength ZF/MMSEDFE results to the infinitelength case. Then, we obtain a general expression for the postSNR of a ZFDFE for the case of infinite length i.i.d. fading channel under the assumption of errorfree decision feedback (ideal DFE). In section 4, we present the limiting analysis for receivers employing WL processing. Collection of complex and complexconjugated copies of the received signal effectively doubles the number of receiver branches. We show that these additional signal copies obtained through WL processing helps the receiver to obtain a substantially higher postSNR compared to conventional LEs. Analogous to the case of conventional ZFDFE, in section 5, we obtain filter settings for the WL ZF/MMSEDFE receiver. Then a general expression for the postSNR of the WL ZFDFE is obtained for the case of infinitelength i.i.d. fading channel. In section 6, we present simulation results. Finally, conclusions are drawn in section 7.
Notation
The following notation is adopted throughout the paper. Vectors are denoted using boldface lowercase letters, matrices are denoted using boldface uppercase letters. Time domain quantities are denoted using the subscript t. The Mpoint DFT of a vector h_{ t }(l) is defined as $\mathbf{h}\left(k\right)=\sum _{l=0}^{M1}{\mathbf{h}}_{t}\left(l\right){e}^{\frac{j2\pi \mathrm{kl}}{M}}$, where k = 0,1,..,M  1. The corresponding Mpoint IDFT is given by ${\mathbf{h}}_{t}\left(l\right)=\frac{1}{M}\sum _{k=0}^{M1}\mathbf{h}\left(k\right){e}^{\frac{j2\pi \mathrm{kl}}{M}}$. The squared Euclidean norm of a row/column vector h(k) = [h_{1}(k),h_{2}(k),..,h_{ n }(k)] is denoted as $\left\right\mathbf{h}\left(k\right){}^{2}=\sum _{m=1}^{n}{h}_{m}\left(k\right){}^{2}$. The circular convolution between two length N sequences is defined as ${x}_{1}\left(n\right)\odot {x}_{2}\left(n\right)=\sum _{n=0}^{N1}{x}_{1}\left(n\right){x}_{2}{\left(\right(mn\left)\right)}_{N}$ where the subscript in x_{2}((mn))_{ N } denotes modulo N operation and ⊙ denotes circular convolution operation. The symbols †, ∗, Tr denote Hermitian, complexconjugate and transpose operations, respectively and E[.] denotes expectation operator.
2 System model
where T is the useful portion of OFDMA symbol, T_{ CP } is the duration of the cyclic prefix (CP) and $\mathrm{\Delta f}=\frac{1}{T}$ is the subcarrier spacing.
3 MMSEDFE receiver
where z_{ t }(l) is obtained after taking the IDFT of z(k) and ${\overrightarrow{z}}_{t}\left(l\right)$ is the ISI free time domain signal which is fed to the symbol demodulator. Here, the symbol ⊖ denotes right circular shift operation.
3.1 Limiting performance of ZFDFE in wideband channels
Note that the variable v is replaced with M in (8) line 2 because as v → ∞, M → ∞, since v < < M. Next, we model h_{ t }(l) as an i.i.d. zeromean, complexGaussian vector with covariance $E\left({\mathbf{h}}_{t}\left(l\right){\mathbf{h}}_{t}^{\u2020}\left(l\right)\right)=\frac{\mathbf{I}}{v}$. Note that pertap power is set to $\frac{1}{v}$ so that the total power contained in the multipath channel becomes unity. As v → ∞, we can express the covariance term as: ${\text{lim}}_{v\to \infty}E\left({\mathbf{h}}_{t}\left(l\right){\mathbf{h}}_{t}^{\u2020}\left(l\right)\right)={\text{lim}}_{v\to \infty}\frac{\mathbf{I}}{v}={\text{lim}}_{M\to \infty}\frac{\mathbf{I}}{M}$. Again here, v is replaced with M in the limit as v → ∞. We have an infinite number of taps with vanishingly small power. However, the sum total power of all the taps is equal to unity. Using (8), it can be shown that h(k) approaches an i.i.d. complex Gaussian vector with zero mean and the covariance tends to an identity matrix, i.e. ${\text{lim}}_{v\to \infty}E\left(\mathbf{h}\left(k\right){\mathbf{h}}^{\u2020}\left(k\right)\right)\to \mathbf{I}$. More specifically, the probability density function of the elements of the channel vector h(k) approaches an i.i.d. complex Gaussian distribution with zero mean and unit variance, and the vectors h(k) become statistically independent for k = 0,1,..,M1.
For comparison, postSNR corresponding to the MFB is given by ${\mathtt{\text{SNR}}}_{\mathtt{\text{MFB}}}=\frac{{N}_{r}}{{\sigma}_{n}^{2}}$.
The above result suggests that highly dispersive nature of the frequencyselective channel can be exploited advantageously to obtain a performance comparable to the MFB. After evaluating the expression (13) for the case of a singlereceiver antenna, the ZFDFE provides a postSNR of $\frac{0.5616}{{\sigma}_{n}^{2}}$ that is 2.5dB less than the MFB. For this case, both ZF and MMSEbased LEs perform poorly compared to the MFB [45]. However, the ZFDFE does not suffer from this limitation and provides a substantial gain over MMSE/ZFLE. For N_{ r }= 2, the loss of ZFDFE with respect to the MFB reduces to 1.19 dB whereas the ZFLE has a higher loss of 3.0 dB.
3.2 DFE initialization
In the MMSEDFE implementation considered in this paper, the feedback filter is implemented in the time domain. In (5), the ISI term $\sum _{m=1}^{L}{b}_{t}\left(m\right){x}_{t}(l\ominus m)$ is obtained by circularly convolving the FBF b_{ t }(l) with the data sequence x_{ t }(l). For detecting the first data symbol x_{ t }(0), the receiver has to eliminate the ISI caused by the last L data symbols of the data sequence x_{ t }(l). Specifically, the DFE requires knowledge of the data symbols x_{ i }= [x_{ t }(NL),..,x_{ t }(N2),x_{ t }(N1)]. As proposed in [28], we use a linear equalizer to obtain hard decisions for the required elements contained in x_{ i }. These symbol estimates are then used to initialize the DFE. Simulation shows that this approach works quite well and the loss in the performance compared to the case of an ideal DFE is acceptable. We would like to remark here that an iterative receiver is presented in [23] to address the DFE initialization problem. The results of this paper show that MMSELEbased initialization is sufficient to obtain nearideal performance. An alternative receiver initialization method is also discussed in [31] for trellisbased receivers. Different iterative block DFE methods have been proposed in [23, 32] for DFEprecodedOFDMA systems. These methods use a linear equalizer in the first iteration, then applies block level decision feedback based on soft decisions in subsequent iterations. In this paper, we are mainly concerned with the analysis of conventional DFEs based on hard decision feedback.
4 Widely linear frequency domain MMSE equalizer
where $D=\frac{1}{M}\sum _{k=0}^{M1}\left[\frac{1}{\frac{{\sigma}_{n}^{2}}{{\sigma}_{x}^{2}}+\left(\left\right\mathbf{h}\left(k\right){}^{2}+\left\mathbf{h}\right(Mk\left)\right{}^{2}\right)}\right]$.
4.1 Liming performance of WL ZFLE
Note that the variance of $\left[\frac{1}{\left[\left\right\mathbf{h}\left(k\right){}^{2}+\left\mathbf{h}\right(Mk\left)\right{}^{2}\right]}\right]$ is bounded only for N_{ r }> 1.
4.1.1 Remark
In Equation 24, h(0)^{2} and $\left\right\mathbf{h}\left(\frac{M}{2}\right){}^{2}$ are sum of squares of N_{ r } i.i.d. complex Gaussian r.v.’s which give a chisquare random variable with 2N_{ r } DOF while [h(k)^{2}+h(Mk)^{2}] has chisquare random variable with 4N_{ r } DOF. For the special case of N_{ r }= 1, the expected value of $\frac{1}{\left\right\mathbf{h}\left(0\right){}^{2}}$ or $\frac{1}{\left\right\mathbf{h}\left(\frac{M}{2}\right){}^{2}}$ is unbounded since it has inverse chisquare distribution with two DOF. However, the mean of $\frac{1}{\left[\left\right\mathbf{h}\left(k\right){}^{2}+\left\mathbf{h}\right(Mk\left)\right{}^{2}\right]}$ is bounded for any value of N_{ r }. In the limiting case as M → ∞, the contribution of the first two terms in (24) vanishes. However, for the special case of N_{ r }= 1, and for finite values of v, h(k) and h(Mk) become correlated random variables. Specifically for values of k = 0 and $k=\frac{M}{2}$, these terms become equal while for values of k in the vicinity of 0 and $\frac{M}{2}$ they become highly correlated. Considering the first two terms of Equation 24, we see that the terms $\frac{1}{\left\right\mathbf{h}\left(0\right){}^{2}}$ or $\frac{1}{\left\right\mathbf{h}\left(\frac{M}{2}\right){}^{2}}$ contribute to an increase in the MSE. Similarly, since h(k) and h(Mk) can be highly correlated for certain subcarrier locations, the term $\frac{1}{\left[\left\right\mathbf{h}\left(k\right){}^{2}+\left\mathbf{h}\right(Mk\left)\right{}^{2}\right]}$ contributes to an increase in MSE for those subcarrier locations. The overall increase in the MSE can be controlled by considering a WL MMSE which regularizes the denominator terms. Simulation is used to quantify the gain of WL MMSELE over ZF case.
5 WL MMSEDFE
5.1 Performance of WL ZFDFE in wideband channels
For N_{ r }= 1, the ideal WL ZFDFE offers a postSNR of $\frac{1.5265}{{\sigma}_{n}^{2}}$ that is 1.17 dB away from the MFB. The actual performance gap with practical FBF is determined using BER simulation.
5.1.1 Remarks

For the case of WL ZF/MMSEDFE, we ignore the potential MSE increase contributed by the terms located at k = 0 and $k=\frac{M}{2}$. Since at these locations, the exponent in (34) involves the terms E[lnh(0)^{2}], $E\left[ln{\left\left\mathbf{h}\left(\frac{M}{2}\right)\right\right}^{2}\right]$ which take a finite value, the overall increase in the MSE can be neglected for finite values of M.

We note here that our main goal of the paper is to expose the basic properties of conventional and WL equalizers in wideband channels. Our aim is not to promote the use of real constellations over typically used complex modulation methods. However, the analysis and results related to WL equalizers are useful in systems where real constellations are employed. One such application is discussed in [52] where binary modulation along with duobinary precoding is employed in the uplink of DFTprecodedOFDM to reduce the PAPR.
6 Results
We present BER simulation results for BPSK and 8PSK and 16QAM (quadrature amplitude modulation) systems. In all cases, the FBF length is set equal to the channel memory. Throughout the paper, we present results for a 20tap i.i.d. Rayleigh fading channel with M=512 in all cases.
6.1 BER results for conventional equalizers
Theoretically expected SNR gap of the receiver with respect to the MFB in decibels (dB)
Receiver type  Gap for N_{ r }= 1  Gap for N_{ r }= 2 

Conv ZFLE  NA  3.0 
Conv ZFDFE  2.5  1.19 
WL ZFLE  3.0  1.25 
WL ZFDFE  1.17  0.5644 
SNR gap of the receiver with respect to MFB in decibels (dB) at BER = 0.01 for BPSK
Receiver type  Gap for N_{ r }= 1  Gap for N_{ r }= 2 

Conv ZFLE  NA  3.0 
Conv ZFDFE  2.5  1.2 
Conv MMSELE  3.4  1.65 
Conv MMSEDFE  1.4  0.75 
WL ZFLE  3.2  1.3 
WL ZFDFE  1.2  0.6 
WL MMSELE  2.15  1.0 
WL MMSEDFE  1.0  0.5 
SNR gap of the receiver with respect to MFB in decibels (dB) at BER = 0.001 for BPSK
Receiver type  Gap for N_{ r }= 1  Gap for N_{ r }= 2 

Conv ZFLE  NA  3.2 
Conv ZFDFE  2.6  1.1 
Conv MMSELE  4.2  2.0 
Conv MMSEDFE  1.7  0.8 
WL ZFLE  3.8  1.4 
WL ZFDFE  1.17  0.6 
WL MMSELE  2.55  1.0 
WL MMSEDFE  1.05  0.5 
6.2 BER of WL equalizers
7 Conclusions
This paper describes the limiting behavior of conventional and WL equalizers in wideband frequencyselective channels. For systems employing DFTprecodedOFDM modulation, closedform expressions are obtained for the postSNR of conventional and WL receivers employing ZFLE and ZFDFE; simulation is used to assess the performance of MMSEbased receivers. In i.i.d. fading channels with infinite channel memory, the postSNR reaches a fixed value that is comparable to the MFB in most cases.
Both conventional MMSELE and ZFLE offer near optimal performance only when the receiver has multiple antennas, whereas ideal ZFDFE and ideal MMSEDFE perform close to the MFB with a fixed SNR penalty even when the receiver has a single antenna. For singleantenna MMSEDFE with decision feedback, the penalty compared to the ideal DFE is approximately 2.0 dB for 16QAM systems at high SNRs. The total gap compared to MFB is 4.5 dB. Lowcomplexity receiver algorithms that further reduce this gap need to be developed. Unlike the single antenna case, the presence of multiple antennas helps the DFEs to reach a performance close to the MFB. Multiplereceiver antennas are also shown to reduce the error propagation of the DFEs.
For singleantenna systems employing realvalued modulation alphabets, WL receiver processing can be used to obtain a performance advantage over conventional receivers. In particular, the WL MMSELE performs within 3.2 to 3.8 dB of the MFB while the WL MMSEDFE reduces the gap with respect to the MFB to 1.0 dB. Results show that the multiantenna WL receivers (both LEs and DFEs) perform very close to the MFB as predicted by the infinite length i.i.d. fading channel model.
We note here that the proposed infinite length i.i.d fading channel model can be used to obtain the limiting performance of MIMO systems employing spatial multiplexing (SM). The analysis has been carried out in [53] for the case of MIMO ZFLE where it is shown that the postSNR of the receiver reaches a constant value of $\frac{{N}_{r}{N}_{t}}{{\sigma}_{n}^{2}}$ for N_{ r }> N_{ t }, where N_{ t } is the SM rate. Extension to the general case of SM employing ZF/MMSEDFEs is yet to be considered.
Endnote
^{a} (A + B C D)^{1} = A^{1} + A^{1}B(C^{1} + D A^{1}B)^{1}D A^{1}
Appendices
Appendix 1
Derivation of MMSEDFE filter settings
where the (l,m)th element of the matrix A is given by A(l,m) = q(ml), b = [b_{ t }(1),b_{ t }(2),..,b_{ t }(L)]^{ T r }, and q = [q(1),q(2),..,q(L + 1)]^{ T r }. The elements of the FBF can be obtained by solving (48). It can be seen that the MSE minimizing solution for the FBF becomes a finite length prediction error filter of order L that whitens the error covariance at the output of the MMSELE. The FBF coefficients can be calculated efficiently using the LevinsonDurbin recursion. The minimum MSE can be obtained by substituting the values of the FBF coefficients in the MSE expression (44). Next, we characterize the MMSEDFE for the case of M→∞.
Appendix 2
Derivation of WL MMSEDFE filter settings
In this section, we discuss the design aspects of WL MMSE DFE. The key implementation differences between conventional and WL equalizers are highlighted. Specifically, we notice that the noise covariance term at the output of the WL MMSE section exhibits even symmetry in frequency domain. This property is exploited to reduce the computational complexity of FFF and FBF filter calculation.
Let $P\left(k\right)=\left\right\mathbf{h}\left(k\right){}^{2}+\left\mathbf{h}\right(Mk\left)\right{}^{2}+\frac{{\sigma}_{n}^{2}}{{\sigma}_{x}^{2}}$. This is a realvalued function which exhibits even symmetry, i.e. P(k) = P(Mk) for k = 0,1,..,M1. Similar to the WL MMSELE case, it is computationally efficient to calculate the filter w(k) explicitly. The second filter can be obtained from the first by applying complex conjugation and frequency reversal operations.
The last term involves $\frac{M}{2}$ point type1 DCT of P(k). Note that $\stackrel{\u0304}{q}\left(l\right)$ needs to be calculated only for the first $\frac{M}{2}$ terms since the rest of the coefficients can be obtained exploiting the even symmetry of $\stackrel{\u0304}{q}\left(l\right)$ i.e., $\stackrel{\u0304}{q}\left(l\right)=\stackrel{\u0304}{q}(Ml)$.
where the (l,m)th element of the matrix $\stackrel{\u0304}{\mathbf{A}}$ denotes as $\u0100(l,m)$ is given by $\u0100(l,m)=\stackrel{\u0304}{q}(ml)$, $\stackrel{\u0304}{\mathbf{b}}={\left[{\stackrel{\u0304}{b}}_{t}\right(1),{\stackrel{\u0304}{b}}_{t}(2),\mathrm{..},{\stackrel{\u0304}{b}}_{t}(L\left)\right]}^{\mathit{\text{Tr}}}$, and $\stackrel{\u0304}{\mathbf{q}}={\left[\stackrel{\u0304}{q}\right(1),\stackrel{\u0304}{q}(2),\mathrm{..},\stackrel{\u0304}{q}(L\left)\right]}^{\mathit{\text{Tr}}}$. Note that FBF can be calculated with low complexity using LevinsonDurbin recursion which involves realvalued quantities whereas the FBF for the conventional case involves complex values. Now we consider the infinite length filter case.
Declarations
Acknowledgements
This work was carried out as part of Converged Cloud Communication Technologies project sponsored by the Department of Electronics and Information Technology (DeitY), Government of India.
Authors’ Affiliations
References
 3GPP: 3GPP TS 36.211 V8.2.0 (200803). [Online]. Available http://www.3gpp.org
 Ciochina C, Mottier D, Sari H: An analysis of three multiple access techniques for the uplink of future cellular mobile systems. European Trans. TeleCommun. 2008., 19:Google Scholar
 3GPP: Physical Layer Aspects for Evolved UTRA (Release 8), 3GPP Std. 2008, TS 36.211.Google Scholar
 Ma X, Zhang W: Fundamental limits of linear equalizers: diversity, capacity, and complexity. IEEE Trans. Inform. Theory 2008, 54: 34423456.MathSciNetView ArticleGoogle Scholar
 Tepedelenlioglu C: Maximum multipath diversity with linear equalization in precoded OFDM systems. IEEE Trans. Inform. Theory 2004, 50: 232235. 10.1109/TIT.2003.821987MathSciNetView ArticleGoogle Scholar
 Hedayat A, Nosratinia A, AlDhahir N: Outage probability and diversity order of linear equalizers in frequencyselective fading channels. Signals, Systems and Computers (ASILOMAR), 2004 2004.Google Scholar
 Shenoy SP, Ghauri I, Slock DT: Diversity order of linear equalizers for doubly selective channels. IEEE International Workshop Signal Processing Advances in Wireless Communications, 2009 2009.Google Scholar
 Song S, Letaief KB: Diversity analysis for linear equalizers over ISI channels. IEEE Trans. Commun. 2004, 59: 24142423.View ArticleGoogle Scholar
 Wang Z, Ma X, Giannakis G: OFDM or singlecarrier block transmissions? IEEE Trans. Commun. 2004, 52: 380394. 10.1109/TCOMM.2004.823586View ArticleGoogle Scholar
 Cioffi J, Dudevoir G, Eyuboglu MV, Forney GD: MMSE decisionfeedback equalizers and codingpart 1: equalization results. IEEE Trans. Commun. 1995, 43: 25822594. 10.1109/26.469441View ArticleGoogle Scholar
 MMSE decisionfeedback equalizers and codingpar 2: coding results IEEE Trans. Commun. 1995, 43: 25952603. 10.1109/26.469440Google Scholar
 Medles A, Slock DT: Decisionfeedback equalization achieves full diversity for finite delay spread channels. Proceedings on the ISIT 2004 2004.Google Scholar
 AlDhahir N: MMSE decisionfeedback equalizers: finite length results. IEEE Trans. Commun. 1995, 41: 961975.Google Scholar
 Eyuboglu MY, Quereshi S: Reduced state sequence estimation with setpartitioning and decision feedback. IEEE Trans. Commun. 1988, 36: 1320. 10.1109/26.2724View ArticleGoogle Scholar
 Forney GD: Maximumlikelihood sequence estimation of digital sequences in the presence of intersymbol interference. IEEE Trans. Inform. Theory 1972, 18: 363378. 10.1109/TIT.1972.1054829MathSciNetView ArticleGoogle Scholar
 Ungerboeck G: Adaptive maximumlikelihood receiver for carriermodulated datatransmission systems. IEEE Trans. Commun. 1974, 22: 624636. 10.1109/TCOM.1974.1092267View ArticleGoogle Scholar
 Gerstacker WH, Schober R: Equalization concepts for EDGE. IEEE Trans. Wireless Commun. 2002, 1: 190199. 10.1109/7693.975457View ArticleGoogle Scholar
 Cioffi J: EE:379 Stanford Class Notes. [Online]. Available: http://www.stanford.edu/class/ee379a/
 Ariyavisitakul SL, Winters JH, Lee I: Optimum spacetime processors with dispersive interference: unified analysis and required filter span. IEEE Trans. Commun. 1999, 47: 10731083. 10.1109/26.774857View ArticleGoogle Scholar
 Fischer RFH: Precoding and Signal Shaping for Digital Transmission. (Wiley)Google Scholar
 Benvenuto N, Tomasin S: On the comparison between OFDM and single carrier modulation with a DFE using a frequencydomain feedforward filter. IEEE Trans. Commun. 2002, 50: 94755. 10.1109/TCOMM.2002.1010614View ArticleGoogle Scholar
 Falconer D, Ariyavisitakul S, BenyaminSeeyar A, Eidson B: in IEEE Commun. Mag.. 2002.Google Scholar
 Benvenuto N, Tomasin S: Iterative design and detection of a DFE in the frequency domain. IEEE Trans. Commun. 2005.Google Scholar
 Dinis R, Falconer D: Iterative block decision feedback equalization techniques (IBDFE) for broadband wireless systems.Google Scholar
 Dinis R, Gusmão A, Esteves N: On broadband block transmission over strongly frequencyselective fading channels.Google Scholar
 Iterative blockdfe techniques for singlecarrierbased broadband communications with transmit/receive space diversityGoogle Scholar
 Dinis R, Carvalho P, Bernardo L, Oliveira R, Pereira M, Pinto P: Frequency domain multipacket detection: a high throughput technique for SCFDE system. IEEE Trans. Wireless Commun. 2005.Google Scholar
 Padmanabhan M, Vinod R, Kuchi K, Giridhar K: MMSE DFE for MIMO DFTspread OFDMA. In National Conference on Communications. Guwahati, India; 2009.Google Scholar
 Prasad N, Wang S, Wang X: Efficient receiver algorithms for DFTspread OFDM systems. IEEE Trans. Wireless Commun. 2009.Google Scholar
 Lin Z, Xiao P, Vucetic V: Analysis of receiver algorithms for LTE SCFDMA based uplink MIMO systems. IEEE Trans. Wireless Commun. 2010.Google Scholar
 Gerstacker W, Nickel P, Obernosterer F, Dang UL, Gunreben P, Koch W: Trellisbased receivers for SCFDMA transmission over MIMO ISI channels. Proceedings of the International Conference on Communications 2008, 45264531.Google Scholar
 Silva A, Assunção J, Dinis R, Gameiro A: Performance evaluation of IBDFEbased strategies for SCFDMA systems. EURASIP J. Wireless Commun. Netw. [Online]. Available: http://jwcn.eurasipjournals.com/content/2013/1/292
 Picinbono B, Chevalier P: Widely linear estimation with complex data. IEEE Trans. Signal Process. 1995, 43: 20302033. 10.1109/78.403373View ArticleGoogle Scholar
 Yoon YC, Leib H: Maximizing SNR in improper complex noise and application to CDMA. IEEE Commun. Lett. 1997, 1: 58.View ArticleGoogle Scholar
 Gelli G, Paura L, Ragozini A: Widely linear multiuser detection. IEEE Commun. Lett. 2000, 4: 187189.View ArticleGoogle Scholar
 Buzzi S, Loops M, Tulino A: A new class of multiuser CDMA receivers based on minimum meanoutputenergy strategy. ISIT 2000.Google Scholar
 Lampe A, Breiling M: Asymptotic analysis of widely linear MMSE multiuser detectioncomplex vs real modulation. Proceedings of the Information Theory Workshop 2001, 5557.Google Scholar
 Lampe A, Schober R, Gerstacker WH, Huber J: A novel iterative multiuser detector for complex modulation schemes. IEEE J. Select. Areas Commun. 2002, 20: 339350. 10.1109/49.983351View ArticleGoogle Scholar
 Gerstacker WH, Schober R, Lampe A: Receivers with widely linear processing for frequencyselective channels. IEEE Trans. Commun. 2003, 51: 15121522. 10.1109/TCOMM.2003.816992View ArticleGoogle Scholar
 Gerstacker WH, Obernosterer F, Schober R, Lehmann A, Lampe A, Gunerben P: Equalization concepts for Alamouti spacetime block code. IEEE Trans. Commun. 2004, 52: 11781190. 10.1109/TCOMM.2004.831357View ArticleGoogle Scholar
 Darsena D, Gelli G, Paura L, Verde F: Widely linear equalization and blind channel identification for interferencecontaminated multicarrier systems. IEEE Trans. Signal Process. 2005, 53: 11631177.MathSciNetView ArticleGoogle Scholar
 Darsena D, Gelli G, Paura L, Verde F: Subspacebased blind channel identification of SISOFIR systems with improper random inputs. in EURASIP J. Signal Process 2004.Google Scholar
 Chevalier P, Pipon F: New insights into optimal widely linear array receivers for demodulation of BPSK, MSK, and GMSK corrupted by noncircular interferersapplication to SAIC. IEEE Trans. Signal Process. 2006, 54: 870883.View ArticleGoogle Scholar
 Trigui H, Slock D: Cochannel interference cancellation within the current GSM standard. Proceedings of the International Conference on Universal Personal Communications 1998, 511515.Google Scholar
 Kuchi K: Limiting behavior of ZF/MMSE linear equalizers in wideband channels with frequency selective fading. IEEE Commun. Lett. 2012, 16: 929932.View ArticleGoogle Scholar
 Darsena D, Gelli G, Paura L, Verde F: Blind channel shortening for asynchronous SCIFDMA systems with CFOs. IEEE Trans. Commun. 2013.Google Scholar
 Darsena D, Gelli G, Verde F: Joint blind channel shortening and compensation of transmitter I/Q imbalances and CFOs for uplink SCIFDMA systems. Elsevier Physical Communication 2014.Google Scholar
 Verde F: Frequencyshift zeroforcing timevarying equalization for doubly selective SIMO channels. Eurasip Journal on Applied Signal Processing 2006.Google Scholar
 Lowcomplexity timevarying frequencyshift equalization for doubly selective channels In Tenth International Symposium on Wireless Communication Systems (ISWCS). Ilmenau, Germany; 2013.Google Scholar
 Oyman O, Nabar R, Bolcskei H, Paulraj A: Characterizing the statistical properties of mutual information in MIMO channels. IEEE Trans. Signal Process. 2003, 51: 278495. 10.1109/TSP.2003.818153MathSciNetView ArticleGoogle Scholar
 Bernardo J, Smith A: Bayesian Theory. Wiley; 1993.Google Scholar
 Kuchi K: Partial response DFTprecodedOFDM modulation. Trans. Emerg. Telecommunications Technol. 2012.Google Scholar
 MMSEprewhitenedMLD equalizer for MIMO DFTprecodedOFDMA IEEE Wireless Commun. Lett. 2012, 1: 328331.Google Scholar
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