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

Som, Pritam; Datta, Tanumay; Srinidhi, N. N.; Chockalingam, A.; Rajan, B. Sundar

doi:10.1109/jstsp.2011.2166950

Cited by 115 publications

(142 citation statements)

References 48 publications

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“…9. Comparison of the BER performance of the proposed CHEMP receiver with those of 1) MMSE detector with MMSE channel estimate, and 2) FG-GAI detector in [11] with MMSE channel estimate, for , 4-QAM.…”

Section: Note On Complexitymentioning

confidence: 99%

“…We compare the performance of the CHEMP receiver with two other receivers, namely, 1) MMSE detector with MMSE channel estimate, and 2) FG-GAI (factor graph with Gaussian approximation of interference) detector in [11] with MMSE channel estimate. We note that the FG-GAI detector in [11] is also a message passing algorithm which used a Gaussian approximation of interference. But this approximation was done on the original system model in (2), whereas in the proposed MPD algorithm, the Gaussian approximation is done on the matched filtered system model in (7).…”

Section: F Ber Performance Of the Chemp Receivermentioning

confidence: 99%

“…9, we present an uncoded BER performance comparison between 1) proposed CHEMP receiver, 2) MMSE detector with MMSE channel estimate, and 3) FG-GAI detector in [11] with MMSE channel estimate. It can be seen that the performance of the proposed CHEMP receiver is significantly better than those of the MMSE and FG-GAI detectors with MMSE estimate of the channel.…”

Section: F Ber Performance Of the Chemp Receivermentioning

confidence: 99%

“…In the recent years, several low complexity detection algorithms which achieve near-optimal performance in large dimensions using complexities comparable to that of MMSE detection have been proposed [1], [2], [6]- [16]. These algorithms are based on local search (e.g., likelihood ascent search (LAS) algorithm and variants in [1], [2], [6], [7]), meta-heuristics (e.g., reactive tabu search (RTS) and variants in [8], [9]), message passing techniques (e.g., belief propagation (BP) based algorithms in [11], [12]), lattice reduction techniques (e.g., lattice reduction (LR) aided detectors in [13], [14]), and Monte-Carlo sampling techniques (e.g., Markov chain Monte Carlo (MCMC) algorithms in [15]). Issues related to channel estimation and low density parity check codes for large-scale MIMO systems are also being addressed [17], [18].…”

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confidence: 99%

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Channel hardening-exploiting message passing (CHEMP) receiver in large MIMO systems

Narasimhan

Chockalingam

2014

2014 IEEE Wireless Communications and Networking Conference (WCNC)

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Abstract-In this paper, we propose a multiple-input multiple-output (MIMO) receiver algorithm that exploits channel hardening that occurs in large MIMO channels. Channel hardening refers to the phenomenon where the off-diagonal terms of the matrix become increasingly weaker compared to the diagonal terms as the size of the channel gain matrix increases. Specifically, we propose a message passing detection (MPD) algorithm which works with the real-valued matched filtered received vector (whose signal term becomes , where is the transmitted vector), and uses a Gaussian approximation on the off-diagonal terms of the matrix. We also propose a simple estimation scheme which directly obtains an estimate of (instead of an estimate of ), which is used as an effective channel estimate in the MPD algorithm. We refer to this receiver as the channel hardening-exploiting message passing (CHEMP) receiver. The proposed CHEMP receiver achieves very good performance in large-scale MIMO systems (e.g., in systems with 16 to 128 uplink users and 128 base station antennas). For the considered large MIMO settings, the complexity of the proposed MPD algorithm is almost the same as or less than that of the minimum mean square error (MMSE) detection. This is because the MPD algorithm does not need a matrix inversion. It also achieves a significantly better performance compared to MMSE and other message passing detection algorithms using MMSE estimate of . Further, we design optimized irregular low density parity check (LDPC) codes specific to the considered large MIMO channel and the CHEMP receiver through EXIT chart matching. The LDPC codes thus obtained achieve improved coded bit error rate performance compared to off-the-shelf irregular LDPC codes.

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Section: Note On Complexitymentioning

confidence: 99%

Section: F Ber Performance Of the Chemp Receivermentioning

confidence: 99%

Section: F Ber Performance Of the Chemp Receivermentioning

confidence: 99%

mentioning

confidence: 99%

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Channel hardening-exploiting message passing (CHEMP) receiver in large MIMO systems

Narasimhan

Chockalingam

2014

2014 IEEE Wireless Communications and Networking Conference (WCNC)

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“…It is natural to seek to improve over linear receivers by considering a committee of linear receivers, and given its success story, AdaBoost is a natural candidate to learn the parameters of such a committee. We note that in recent years, ideas from machine learning have been used in communications [18], [19], [20], [21], [22]. In particular, [22] proposes joint channel estimation and multiuser detection based on a "total least squares" approach.…”

Section: Introductionmentioning

confidence: 99%

Boosting MMSE Receivers Using AdaBoost

Dabeer

Nagaraja

Chockalingam

2013

2013 IEEE 78th Vehicular Technology Conference (VTC Fall)

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Abstract-MIMO systems with several antennas at the transmitter and receiver have the potential to enable high throughputs. One of the challenges in realizing this potential is the design of receivers that scale in terms of computation and performance. Towards this end, in this paper, we propose a receiver that uses a committee of linear receivers, whose parameters are estimated from training data using a variant of the AdaBoost algorithm, a celebrated supervised classification algorithm in machine learning. We call our receiver boosted MMSE (B-MMSE) receiver and we study its performance via simulations. The channel matrix is estimated from the pilot using the MMSE technique. We find that for a 4 × 4 system with a Rayleigh fading channel, using BPSK at a bit error rate (BER) of 10 −2 , our receiver using the estimated channel matrix needs 2 dB less power than the plain MMSE receiver based on the estimated channel matrix. On an average, excluding the training phase, the receiver complexity equals 1.25 linear receivers. Thus the B-MMSE receiver is slightly more complex than the MMSE receiver and substantially less complex than the maximum-likelihood receiver. We also show that the coded BER performance with an outer rate-3/4 Turbo-code has a gain of 5 dB at 10 −4 coded BER when the demodulated output from the proposed method is used in place of plain MMSE detection with MMSE estimated channel.

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Bit error rate aware accurate prediction of original signals with low distortion using low complexity detection algorithms

Kasiselvanathan¹,

Kumar²

2019

Concurrency and Computation

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Summary In the Massive MIMO system, complexity reduction acts as a significant role. This problem is focused in previous method whose foremost objective is to attain the low complexity. The main goal of this algorithm is to process the signal vector with more maximum likelihood cost instead of handling every signals vector, which might lead to more iteration. This is done by introducing different heuristic algorithms which attempts to find the signal vectors with more maximum likelihood values. The algorithms introduced in the existing work are QP detector and the Branch and Bound algorithm. The existing work still poses more complexity by taking more number of iterations for finding the more relative signal vector. These problems are resolved in the proposed system by introducing the novel framework, namely, pruning‐based maximum likelihood detection using low complexity detection algorithms (PRUN‐MLD‐LCDA). In the proposed system, complexity of the system is further reduced by introducing the cross correlation based pruning technique which attempts to find the similar signal, which is similar based on more signal components. The modified branch and bound algorithm is introduced to perform low complexity detection for the different QAM modulation techniques. The overall evaluation of the proposed research work is done in the Matlab simulation environment from which it is proved that the proposed research method leads to provide better result than the existing research methods.

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

Cited by 115 publications

References 48 publications

Channel hardening-exploiting message passing (CHEMP) receiver in large MIMO systems

Channel hardening-exploiting message passing (CHEMP) receiver in large MIMO systems

Boosting MMSE Receivers Using AdaBoost

Bit error rate aware accurate prediction of original signals with low distortion using low complexity detection algorithms

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