Markov chain Monte Carlo algorithms for CDMA and MIMO communication systems

Farhang‐Boroujeny, Behrouz; Zhu, Huangtianzhi; Shi, Zhenning

doi:10.1109/tsp.2006.872539

Cited by 151 publications

(174 citation statements)

References 49 publications

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“…Fig. 2 suggests that our HS-aided algorithm with less number of FF evaluations exhibit a lower error floor than that of the conventional MH method, which is often found at the high SNR [5]. 1 Fig .…”

Section: B Simulation Resultsmentioning

confidence: 89%

“…Hence, the random-walk behaviour of the MH method may be avoided. This is the fundamental difference in comparison to the MC based algorithms, such as [5]. On the other hand, our HS algorithm is also different from the traditional importance sampling or rejection sampling technique [3], which requires a so-called proposal distribution and is only effective in low-dimensional problems.…”

Section: Commentsmentioning

confidence: 98%

“…We note that all possible vectors are required in the above summation, which leads to a prohibitive complexity. This motivates the development of a range of low-complexity MUD detection algorithms, such as those in [2] and [5] and the proposed HS algorithm of the next section.…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

See 2 more Smart Citations

Iterative Multiuser Detection and Channel Decoding for DS-CDMA Using Harmony Search

Zhang

Hanzo

2009

IEEE Signal Process. Lett.

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Abstract-A novel random-guided optimization method is proposed for multiuser detection (MUD) in DS-CDMA systems employing the so-called Harmony Search (HS) algorithm. We specifically design the HS-aided MUD for the communications problem considered and apply it in an iterative joint MUD and channel decoding framework. Our simulation results demonstrate that a near-single-user performance can be achieved without the employment of the full-search-based optimum MUD even in extremely highly loaded DS-CDMA systems.Index Terms-Evolutionary Algorithm, , Harmony Search Algorithm, iterative receiver, Markov Chain Monte Carlo Algorithm, multiuser detection, .

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Section: B Simulation Resultsmentioning

confidence: 89%

Section: Commentsmentioning

confidence: 98%

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Iterative Multiuser Detection and Channel Decoding for DS-CDMA Using Harmony Search

Zhang

Hanzo

2009

IEEE Signal Process. Lett.

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“…Such detectors offer a low-complexity approximation to the maximum-likelihood sequence estimation (MLSE) and are used to directly perform data detection without channel equalization. The MCMC detectors have been studied previously in [4]- [6] for both multiple-input multipleoutput (MIMO) frequency-flat channels and for frequency selective channels with inter-symbol interference [7]. Under the assumption of perfect channel state information (CSI) at the receiver, the MCMC detectors developed in these work demonstrate excellent performance at low-complexity.…”

Section: Introductionmentioning

confidence: 99%

Markov Chain Monte Carlo detection for underwater acoustic channels

Wan

Chen

Choi

et al. 2010

2010 Information Theory and Applications Workshop (ITA)

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Abstract-In this work, we develop novel statistical detectors to combat intersymbol interference for frequency selective channels based on Markov Chain Monte Carlo (MCMC) techniques. While the optimal maximum a posteriori (MAP) detector has a complexity that grows exponentially with the constellation size and the memory of the channel, the MCMC detector can achieve near optimal performance with a complexity that grows linearly. This makes the MCMC detector particularly attractive for underwater acoustic channels with long delay spread. We examine the effectiveness of the MCMC detector using actual data collected from underwater experiments. When combined with adaptive least mean square (LMS) channel estimation, the MCMC detector achieves superior performance over the direct adaptation LMS turbo equalizers (LMS-TEQ) for a majority of data sets transmitted over distances from 60 meters to 1000 meters.

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“…We address the issue of low-complexity detection in large MIMO systems here. More recent approaches to lowcomplexity multiuser detection and MIMO detection involve application of techniques from belief propagation [2], neural networks [3],[4], Markov chain Monte-Carlo methods [5], probabilistic data association [6], etc. Detectors based on these techniques have been shown to achieve an average perbit complexity linear in number of users, while achieving near-optimal performance in large multiuser CDMA system settings.…”

Section: Introductionmentioning

confidence: 99%

A Low-complexity near-ML performance achieving algorithm for large MIMO detection

Mohammed

Chockalingam

Rajan

2008

2008 IEEE International Symposium on Information Theory

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Abstract-In this paper, we present a low-complexity, near maximum-likelihood (ML) performance achieving detector for large MIMO systems having tens of transmit and receive antennas. Such large MIMO systems are of interest because of the high spectral efficiencies possible in such systems. The proposed detection algorithm, termed as multistage likelihood-ascent search (M-LAS) algorithm, is rooted in Hopfield neural networks, and is shown to possess excellent performance as well as complexity attributes. In terms of performance, in a 64 × 64 V-BLAST system with 4-QAM, the proposed algorithm achieves an uncoded BER of 10 −3 at an SNR of just about 1 dB away from AWGN-only SISO performance given by Q( √ SNR). In terms of coded BER, with a rate-3/4 turbo code at a spectral efficiency of 96 bps/Hz the algorithm performs close to within about 4.5 dB from theoretical capacity, which is remarkable in terms of both high spectral efficiency as well as nearness to theoretical capacity. Our simulation results show that the above performance is achieved with a complexity of just O(NtNr) per symbol, where Nt and Nr denote the number of transmit and receive antennas. I. INTRODUCTIONMIMO techniques have become popular in realizing spatial diversity and high data rates through the use of multiple transmit antennas [1]. We consider large MIMO systems with tens of transmit and receive antennas, which are of interest due to the high spectral efficiencies possible in such systems. The key issues in realizing large MIMO systems include lowcomplexity detection, channel estimation, and communication terminal size to accommodate large number of antennas. We address the issue of low-complexity detection in large MIMO systems here. More recent approaches to lowcomplexity multiuser detection and MIMO detection involve application of techniques from belief propagation [2], neural networks [3],[4], Markov chain Monte-Carlo methods [5], probabilistic data association [6], etc. Detectors based on these techniques have been shown to achieve an average perbit complexity linear in number of users, while achieving near-optimal performance in large multiuser CDMA system settings. These powerful techniques are increasingly being adopted in MIMO detection. In [4], we presented a Hopfield neural network based likelihood ascent search (LAS) algorithm for large MIMO detection; we showed that the LAS detector achieves near-AWGN SISO performance in a large MIMO setting with hundreds of antennas, while performing close to within 4.6 dB from theoretical capacity.

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Markov chain Monte Carlo algorithms for CDMA and MIMO communication systems

Cited by 151 publications

References 49 publications

Iterative Multiuser Detection and Channel Decoding for DS-CDMA Using Harmony Search

Iterative Multiuser Detection and Channel Decoding for DS-CDMA Using Harmony Search

Markov Chain Monte Carlo detection for underwater acoustic channels

A Low-complexity near-ML performance achieving algorithm for large MIMO detection

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