arXiv subjects

A. Chockalingam

Publications and source records attributed to A. Chockalingam.

53 records · Page 3Linked to original sources

arXiv2012

A Novel MCMC Based Receiver for Large-Scale Uplink Multiuser MIMO Systems

In this paper, we propose low complexity algorithms based on Markov chain Monte Carlo (MCMC) technique for signal detection and channel estimation on the uplink in large scale multiuser multiple input multiple output (MIMO) systems with tens to hundreds of antennas at the base station (BS) and similar number of uplink users. A BS receiver that employs a randomized sampling method (which makes a probabilistic choice between Gibbs sampling and random sampling in each iteration) for detection and a Gibbs sampling based method for channel estimation is proposed. The algorithm proposed for detection alleviates the stalling problem encountered at high SNRs in conventional MCMC algorithm and achieves near-optimal performance in large systems. A novel ingredient in the detection algorithm that is responsible for achieving near-optimal performance at low complexities is the joint use of a {\it randomized MCMC (R-MCMC) strategy} coupled with a {\it multiple restart strategy} with an efficient restart criterion. Near-optimal detection performance is demonstrated for large number of BS antennas and users (e.g., 64, 128, 256 BS antennas/users). The proposed MCMC based channel estimation algorithm refines an initial estimate of the channel obtained during pilot phase through iterations with R-MCMC detection during data phase. In time division duplex (TDD) systems where channel reciprocity holds, these channel estimates can be used for multiuser MIMO precoding on the downlink. Further, we employ this receiver architecture in the frequency domain for receiving cyclic prefixed single carrier (CPSC) signals on frequency selective fading between users and the BS. The proposed receiver achieves performance that is near optimal and close to that achieved with perfect channel knowledge.

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arXiv2011

Full-Rate Full-Diversity Achieving MIMO Precoding with Partial CSIT

In this paper, we consider a $n_t\times n_r$ multiple-input multiple-output (MIMO) channel subjected to block fading. Reliability (in terms of achieved diversity order) and rate (in number of symbols transmitted per channel use) are of interest in such channels. We propose a new precoding scheme which achieves both full diversity ($n_tn_r$th order diversity) as well as full rate ($n_t$ symbols per channel use) using partial channel state information at the transmitter (CSIT), applicable in MIMO systems including $n_r<n_t$ asymmetric MIMO. The proposed scheme achieves full diversity and improved coding gain through an optimization over the choice of constellation sets. The optimization maximizes $d_{min}^2$ for our precoding scheme subject to an energy constraint. The scheme requires feedback of $n_t-1$ angle parameter values, compared to $2n_tn_r$ real coefficients in case of full CSIT. Error rate performance results for $3\times 1$, $3\times 2$, $4\times 1$, $8\times 1$ precoded MIMO systems (with $n_t=3,3,4,8$ symbols per channel use, respectively) show that the proposed precoding achieves 3rd, 6th, 4th and 8th order diversities, respectively. These performances are shown to be better than other precoding schemes in the literature; the better performance is due to the choice of the signal sets and the feedback angles in the proposed scheme.

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arXiv2011

Achievable Rate Region of Quantized Broadcast and MAC Channels

In this paper, we study the achievable rate region of Gaussian multiuser channels with the messages transmitted being from finite input alphabets and the outputs being {\em quantized at the receiver}. In particular, we focus on the achievable rate region of $i)$ Gaussian broadcast channel (GBC) and $ii)$ Gaussian multiple access channel (GMAC). First, we study the achievable rate region of two-user GBC when the messages to be transmitted to both the users take values from finite signal sets and the received signal is quantized at both the users. We refer to this channel as {\em quantized broadcast channel (QBC)}. We observe that the capacity region defined for a GBC does not carry over as such to QBC. We show that the optimal decoding scheme for GBC (i.e., high SNR user doing successive decoding and low SNR user decoding its message alone) is not optimal for QBC. We then propose an achievable rate region for QBC based on two different schemes. We present achievable rate region results for the case of uniform quantization at the receivers. Next, we investigate the achievable rate region of two-user GMAC with finite input alphabet and quantized receiver output. We refer to this channel as {\em quantized multiple access channel (QMAC)}. We derive expressions for the achievable rate region of a two-user QMAC. We show that, with finite input alphabet, the achievable rate region with the commonly used uniform receiver quantizer has a significant loss compared to the achievable rate region without receiver quantization. We propose a {\em non-uniform quantizer} which has a significantly larger rate region compared to what is achieved with a uniform quantizer in QMAC.

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arXiv2011

Low-Complexity Detection/Equalization in Large-Dimension MIMO-ISI Channels Using Graphical Models

In this paper, we deal with low-complexity near-optimal detection/equalization in large-dimension multiple-input multiple-output inter-symbol interference (MIMO-ISI) channels using message passing on graphical models. A key contribution in the paper is the demonstration that near-optimal performance in MIMO-ISI channels with large dimensions can be achieved at low complexities through simple yet effective simplifications/approximations, although the graphical models that represent MIMO-ISI channels are fully/densely connected (loopy graphs). These include 1) use of Markov Random Field (MRF) based graphical model with pairwise interaction, in conjunction with {\em message/belief damping}, and 2) use of Factor Graph (FG) based graphical model with {\em Gaussian approximation of interference} (GAI). The per-symbol complexities are $O(K^2n_t^2)$ and $O(Kn_t)$ for the MRF and the FG with GAI approaches, respectively, where $K$ and $n_t$ denote the number of channel uses per frame, and number of transmit antennas, respectively. These low-complexities are quite attractive for large dimensions, i.e., for large $Kn_t$. From a performance perspective, these algorithms are even more interesting in large-dimensions since they achieve increasingly closer to optimum detection performance for increasing $Kn_t$. Also, we show that these message passing algorithms can be used in an iterative manner with local neighborhood search algorithms to improve the reliability/performance of $M$-QAM symbol detection.

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arXiv2010

A Hybrid RTS-BP Algorithm for Improved Detection of Large-MIMO M-QAM Signals

Low-complexity near-optimal detection of large-MIMO signals has attracted recent research. Recently, we proposed a local neighborhood search algorithm, namely `reactive tabu search' (RTS) algorithm, as well as a factor-graph based `belief propagation' (BP) algorithm for low-complexity large-MIMO detection. The motivation for the present work arises from the following two observations on the above two algorithms: $i)$ RTS works for general M-QAM. Although RTS was shown to achieve close to optimal performance for 4-QAM in large dimensions, significant performance improvement was still possible for higher-order QAM (e.g., 16- and 64-QAM). ii) BP also was shown to achieve near-optimal performance for large dimensions, but only for $\{\pm 1\}$ alphabet. In this paper, we improve the large-MIMO detection performance of higher-order QAM signals by using a hybrid algorithm that employs RTS and BP. In particular, motivated by the observation that when a detection error occurs at the RTS output, the least significant bits (LSB) of the symbols are mostly in error, we propose to first reconstruct and cancel the interference due to bits other than LSBs at the RTS output and feed the interference cancelled received signal to the BP algorithm to improve the reliability of the LSBs. The output of the BP is then fed back to RTS for the next iteration. Our simulation results show that in a 32 x 32 V-BLAST system, the proposed RTS-BP algorithm performs better than RTS by about 3.5 dB at $10^{-3}$ uncoded BER and by about 2.5 dB at $3\times 10^{-4}$ rate-3/4 turbo coded BER with 64-QAM at the same order of complexity as RTS. We also illustrate the performance of large-MIMO detection in frequency-selective fading channels.

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arXiv2009

Near-ML Signal Detection in Large-Dimension Linear Vector Channels Using Reactive Tabu Search

Low-complexity near-optimal signal detection in large dimensional communication systems is a challenge. In this paper, we present a reactive tabu search (RTS) algorithm, a heuristic based combinatorial optimization technique, to achieve low-complexity near-maximum likelihood (ML) signal detection in linear vector channels with large dimensions. Two practically important large-dimension linear vector channels are considered: i) multiple-input multiple-output (MIMO) channels with large number (tens) of transmit and receive antennas, and ii) severely delay-spread MIMO inter-symbol interference (ISI) channels with large number (tens to hundreds) of multipath components. These channels are of interest because the former offers the benefit of increased spectral efficiency (several tens of bps/Hz) and the latter offers the benefit of high time-diversity orders. Our simulation results show that, while algorithms including variants of sphere decoding do not scale well for large dimensions, the proposed RTS algorithm scales well for signal detection in large dimensions while achieving increasingly closer to ML performance for increasing number of dimensions.

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arXiv2009

Asymptotic Analysis of the Performance of LAS Algorithm for Large-MIMO Detection

In our recent work, we reported an exhaustive study on the simulated bit error rate (BER) performance of a low-complexity likelihood ascent search (LAS) algorithm for detection in large multiple-input multiple-output (MIMO) systems with large number of antennas that achieve high spectral efficiencies. Though the algorithm was shown to achieve increasingly closer to near maximum-likelihood (ML) performance through simulations, no BER analysis was reported. Here, we extend our work on LAS and report an asymptotic BER analysis of the LAS algorithm in the large system limit, where $N_t,N_r \to \infty$ with $N_t=N_r$, where $N_t$ and $N_r$ are the number of transmit and receive antennas. We prove that the error performance of the LAS detector in V-BLAST with 4-QAM in i.i.d. Rayleigh fading converges to that of the ML detector as $N_t,N_r \to \infty$.

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arXiv2009

Robust THP Transceiver Designs for Multiuser MIMO Downlink with Imperfect CSIT

In this paper, we present robust joint non-linear transceiver designs for multiuser multiple-input multiple-output (MIMO) downlink in the presence of imperfections in the channel state information at the transmitter (CSIT). The base station (BS) is equipped with multiple transmit antennas, and each user terminal is equipped with one or more receive antennas. The BS employs Tomlinson-Harashima precoding (THP) for inter-user interference pre-cancellation at the transmitter. We consider robust transceiver designs that jointly optimize the transmit THP filters and receive filter for two models of CSIT errors. The first model is a stochastic error (SE) model, where the CSIT error is Gaussian-distributed. This model is applicable when the CSIT error is dominated by channel estimation error. In this case, the proposed robust transceiver design seeks to minimize a stochastic function of the sum mean square error (SMSE) under a constraint on the total BS transmit power. We propose an iterative algorithm to solve this problem. The other model we consider is a norm-bounded error (NBE) model, where the CSIT error can be specified by an uncertainty set. This model is applicable when the CSIT error is dominated by quantization errors. In this case, we consider a worst-case design. For this model, we consider robust i) minimum SMSE, ii) MSE-constrained, and iii) MSE-balancing transceiver designs. We propose iterative algorithms to solve these problems, wherein each iteration involves a pair of semi-definite programs (SDP). Further, we consider an extension of the proposed algorithm to the case with per-antenna power constraints.

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arXiv2009

High-Rate Space-Time Coded Large MIMO Systems: Low-Complexity Detection and Channel Estimation

In this paper, we present a low-complexity algorithm for detection in high-rate, non-orthogonal space-time block coded (STBC) large-MIMO systems that achieve high spectral efficiencies of the order of tens of bps/Hz. We also present a training-based iterative detection/channel estimation scheme for such large STBC MIMO systems. Our simulation results show that excellent bit error rate and nearness-to-capacity performance are achieved by the proposed multistage likelihood ascent search (M-LAS) detector in conjunction with the proposed iterative detection/channel estimation scheme at low complexities. The fact that we could show such good results for large STBCs like 16x16 and 32x32 STBCs from Cyclic Division Algebras (CDA) operating at spectral efficiencies in excess of 20 bps/Hz (even after accounting for the overheads meant for pilot based training for channel estimation and turbo coding) establishes the effectiveness of the proposed detector and channel estimator. We decode perfect codes of large dimensions using the proposed detector. With the feasibility of such a low-complexity detection/channel estimation scheme, large-MIMO systems with tens of antennas operating at several tens of bps/Hz spectral efficiencies can become practical, enabling interesting high data rate wireless applications.

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arXiv2009

FRFD MIMO Systems: Precoded V-BLAST with Limited Feedback Versus Non-orthogonal STBC MIMO

Full-rate (FR) and full-diversity (FD) are attractive features in MIMO systems. We refer to systems which achieve both FR and FD simultaneously as FRFD systems. Non-orthogonal STBCs can achieve FRFD without feedback, but their ML decoding complexities are high. V-BLAST without precoding achieves FR but not FD. FRFD can be achieved in V-BLAST through precoding given full channel state information at the transmitter (CSIT). However, with limited feedback precoding, V-BLAST achieves FD, but with some rate loss. Our contribution in this paper is two-fold: $i)$ we propose a limited feedback (LFB) precoding scheme which achieves FRFD in $2\times 2$, $3\times 3$ and $4\times 4$ V-BLAST systems (we refer to this scheme as FRFD-VBLAST-LFB scheme), and $ii)$ comparing the performances of the FRFD-VBLAST-LFB scheme and non-orthogonal STBCs without feedback (e.g., Golden code, perfect codes) under ML decoding, we show that in $2\times 2$ MIMO system with 4-QAM/16-QAM, FRFD-VBLAST-LFB scheme outperforms the Golden code by about 0.6 dB; in $3\times 3$ and $4\times 4$ MIMO systems, the performance of FRFD-VBLAST-LFB scheme is comparable to the performance of perfect codes. The FRFD-VBLAST-LFB scheme is attractive because 1) ML decoding becomes less complex compared to that of non-orthogonal STBCs, 2) the number of feedback bits required to achieve the above performance is small, 3) in slow-fading, it is adequate to send feedback bits only occasionally, and 4) in most practical wireless systems feedback channel is often available (e.g., for adaptive modulation, rate/power control).

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arXiv2009

Low-Complexity Near-ML Decoding of Large Non-Orthogonal STBCs using Reactive Tabu Search

Non-orthogonal space-time block codes (STBC) with {\em large dimensions} are attractive because they can simultaneously achieve both high spectral efficiencies (same spectral efficiency as in V-BLAST for a given number of transmit antennas) {\em as well as} full transmit diversity. Decoding of non-orthogonal STBCs with large dimensions has been a challenge. In this paper, we present a reactive tabu search (RTS) based algorithm for decoding non-orthogonal STBCs from cyclic division algebras (CDA) having large dimensions. Under i.i.d fading and perfect channel state information at the receiver (CSIR), our simulation results show that RTS based decoding of $12\times 12$ STBC from CDA and 4-QAM with 288 real dimensions achieves $i)$ $10^{-3}$ uncoded BER at an SNR of just 0.5 dB away from SISO AWGN performance, and $ii)$ a coded BER performance close to within about 5 dB of the theoretical MIMO capacity, using rate-3/4 turbo code at a spectral efficiency of 18 bps/Hz. RTS is shown to achieve near SISO AWGN performance with less number of dimensions than with LAS algorithm (which we reported recently) at some extra complexity than LAS. We also report good BER performance of RTS when i.i.d fading and perfect CSIR assumptions are relaxed by considering a spatially correlated MIMO channel model, and by using a training based iterative RTS decoding/channel estimation scheme.

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arXiv2009

Belief Propagation Based Decoding of Large Non-Orthogonal STBCs

In this paper, we present a belief propagation (BP) based algorithm for decoding non-orthogonal space-time block codes (STBC) from cyclic division algebras (CDA) having {\em large dimensions}. The proposed approach involves message passing on Markov random field (MRF) representation of the STBC MIMO system. Adoption of BP approach to decode non-orthogonal STBCs of large dimensions has not been reported so far. Our simulation results show that the proposed BP-based decoding achieves increasingly closer to SISO AWGN performance for increased number of dimensions. In addition, it also achieves near-capacity turbo coded BER performance; for e.g., with BP decoding of $24\times 24$ STBC from CDA using BPSK (i.e., 576 real dimensions) and rate-1/2 turbo code (i.e., 12 bps/Hz spectral efficiency), coded BER performance close to within just about 2.5 dB from the theoretical MIMO capacity is achieved.

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arXiv2009

Low-Complexity Near-ML Decoding of Large Non-Orthogonal STBCs Using PDA

Non-orthogonal space-time block codes (STBC) from cyclic division algebras (CDA) having large dimensions are attractive because they can simultaneously achieve both high spectral efficiencies (same spectral efficiency as in V-BLAST for a given number of transmit antennas) {\em as well as} full transmit diversity. Decoding of non-orthogonal STBCs with hundreds of dimensions has been a challenge. In this paper, we present a probabilistic data association (PDA) based algorithm for decoding non-orthogonal STBCs with large dimensions. Our simulation results show that the proposed PDA-based algorithm achieves near SISO AWGN uncoded BER as well as near-capacity coded BER (within about 5 dB of the theoretical capacity) for large non-orthogonal STBCs from CDA. We study the effect of spatial correlation on the BER, and show that the performance loss due to spatial correlation can be alleviated by providing more receive spatial dimensions. We report good BER performance when a training-based iterative decoding/channel estimation is used (instead of assuming perfect channel knowledge) in channels with large coherence times. A comparison of the performances of the PDA algorithm and the likelihood ascent search (LAS) algorithm (reported in our recent work) is also presented.

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arXiv2008

Single-Symbol ML Decodable Distributed STBCs for Partially-Coherent Cooperative Networks

Space-time block codes (STBCs) that are single-symbol decodable (SSD) in a co-located multiple antenna setting need not be SSD in a distributed cooperative communication setting. A relay network with N relays and a single source-destination pair is called a partially-coherent relay channel (PCRC) if the destination has perfect channel state information (CSI) of all the channels and the relays have only the phase information of the source-to-relay channels. In this paper, first, a new set of necessary and sufficient conditions for a STBC to be SSD for co-located multiple antenna communication is obtained. Then, this is extended to a set of necessary and sufficient conditions for a distributed STBC (DSTBC) to be SSD for a PCRC, by identifying the additional conditions. Using this, several SSD DSTBCs for PCRC are identified among the known classes of STBCs. It is proved that even if a SSD STBC for a co-located MIMO channel does not satisfy the additional conditions for the code to be SSD for a PCRC, single-symbol decoding of it in a PCRC gives full-diversity and only coding gain is lost. It is shown that when a DSTBC is SSD for a PCRC, then arbitrary coordinate interleaving of the in-phase and quadrature-phase components of the variables does not disturb its SSD property for PCRC. Finally, it is shown that the possibility of {\em channel phase compensation} operation at the relay nodes using partial CSI at the relays increases the possible rate of SSD DSTBCs from $\frac{2}{N}$ when the relays do not have CSI to 1/2, which is independent of N.

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arXiv2008

Robust Precoder for Multiuser MISO Downlink with SINR Constraints

In this paper, we consider linear precoding with SINR constraints for the downlink of a multiuser MISO (multiple-input single-output) communication system in the presence of imperfect channel state information (CSI). The base station is equipped with multiple transmit antennas and each user terminal is equipped with a single receive antenna. We propose a robust design of linear precoder which transmits minimum power to provide the required SINR at the user terminals when the true channel state lies in a region of a given size around the channel state available at the transmitter. We show that this design problem can be formulated as a Second Order Cone Program (SOCP) which can be solved efficiently. We compare the performance of the proposed design with some of the robust designs reported in the literature. Simulation results show that the proposed robust design provides better performance with reduced complexity.

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arXiv2008

Large MIMO Detection: A Low-Complexity Detector at High Spectral Efficiencies

We consider large MIMO systems, where by `{\em large}' we mean number of transmit and receive antennas of the order of tens to hundreds. Such large MIMO systems will be of immense interest because of the very high spectral efficiencies possible in such systems. We present a low-complexity detector which achieves uncoded near-exponential diversity performance for hundreds of antennas (i.e., achieves near SISO AWGN performance in a large MIMO fading environment) with an average per-bit complexity of just $O(N_tN_r)$, where $N_t$ and $N_r$ denote the number of transmit and receive antennas, respectively. With an outer turbo code, the proposed detector achieves good coded bit error performance as well. For example, in a 600 transmit and 600 receive antennas V-BLAST system with a high spectral efficiency of 200 bps/Hz (using BPSK and rate-1/3 turbo code), our simulation results show that the proposed detector performs close to within about 4.6 dB from theoretical capacity. We also adopt the proposed detector for the low-complexity decoding of high-rate non-orthogonal space-time block codes (STBC) from division algebras (DA). For example, we have decoded the $16\times 16$ full-rate non-orthogonal STBC from DA using the proposed detector and show that it performs close to within about 5.5 dB of the capacity using 4-QAM and rate-3/4 turbo code at a spectral efficiency of 24 bps/Hz. The practical feasibility of the proposed high-performance low-complexity detector could potentially trigger wide interest in the implementation of large MIMO systems. In large MC-CDMA systems with hundreds of users, the proposed detector is shown to achieve near single-user performance at an average per-bit complexity linear in number of users, which is quite appealing for its use in practical CDMA systems.

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arXiv2007

Improved Linear Parallel Interference Cancellers

In this paper, taking the view that a linear parallel interference canceller (LPIC) can be seen as a linear matrix filter, we propose new linear matrix filters that can result in improved bit error performance compared to other LPICs in the literature. The motivation for the proposed filters arises from the possibility of avoiding the generation of certain interference and noise terms in a given stage that would have been present in a conventional LPIC (CLPIC). In the proposed filters, we achieve such avoidance of the generation of interference and noise terms in a given stage by simply making the diagonal elements of a certain matrix in that stage equal to zero. Hence, the proposed filters do not require additional complexity compared to the CLPIC, and they can allow achieving a certain error performance using fewer LPIC stages. We also extend the proposed matrix filter solutions to a multicarrier DS-CDMA system, where we consider two types of receivers. In one receiver (referred to as Type-I receiver), LPIC is performed on each subcarrier first, followed by multicarrier combining (MCC). In the other receiver (called Type-II receiver), MCC is performed first, followed by LPIC. We show that in both Type-I and Type-II receivers, the proposed matrix filters outperform other matrix filters. Also, Type-II receiver performs better than Type-I receiver because of enhanced accuracy of the interference estimates achieved due to frequency diversity offered by MCC.

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