Gaussian approximation based mixture reduction for near optimum detection in MIMO systems

Y Jia; C Andrieu; RJ Piechocki; M Sandell

doi:10.1109/LCOMM.2005.11018

Gaussian approximation based mixture reduction for near optimum detection in MIMO systems

Y Jia, C Andrieu, RJ Piechocki, M Sandell

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Abstract

The optimal "soft" symbol detection for spatial multiplexing multiple input multiple output (MIMO) system with known channel information requires knowledge of the marginal posterior symbol probabilities for each antenna. The calculation of these quantities requires the evaluation of the likelihood function of the system for all possible symbol combinations, which is prohibitive for large systems. It is however most often the case that most of the transmitted symbol combinations contribute only very little to these marginal posterior probabilities. We propose in this paper a suboptimal procedure which identifies the most significant symbol combinations via a sequential algorithm with Gaussian Approximation (SGA). Simulation results show that our method can approach the optimal a posteriori probability detector (APP) performance while being less complex than comparable suboptimal algorithms, such as the sphere decoder (SD). We further demonstrate that as opposed to the SD the complexity and memory requirements of our algorithm are fixed, therefore easing practical implementation

Translated title of the contribution	Gaussian approximation based mixture reduction for near optimum detection in MIMO systems
Original language	English
Pages (from-to)	997 - 999
Number of pages	3
Journal	IEEE Communications Letters
Volume	9
Issue number	11
DOIs	https://doi.org/10.1109/LCOMM.2005.11018
Publication status	Published - Nov 2005

Bibliographical note

Publisher: IEEE-Inst Electrical Electronics Engineers Inc
Other identifier: IDS Number: 982FG
Rose publication type: Journal article

Sponsorship: The authors wish to thank Toshiba TREL Bristol UK for sponsoring the work presented in this paper and Dr. Mong Suan Yee for helpful discussions concerning the SD

Terms of use: Copyright © 2005 IEEE. Reprinted from IEEE Communications Letters. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Bristol's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org.

By choosing to view this document, you agree to all provisions of the copyright laws protecting it.

Keywords

space-time processing
multiuser detection
Gaussian approximation
probability data association

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Jia IEEE ComsLets 2005Accepted author manuscript, 227 KB

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title = "Gaussian approximation based mixture reduction for near optimum detection in MIMO systems",

abstract = "The optimal {"}soft{"} symbol detection for spatial multiplexing multiple input multiple output (MIMO) system with known channel information requires knowledge of the marginal posterior symbol probabilities for each antenna. The calculation of these quantities requires the evaluation of the likelihood function of the system for all possible symbol combinations, which is prohibitive for large systems. It is however most often the case that most of the transmitted symbol combinations contribute only very little to these marginal posterior probabilities. We propose in this paper a suboptimal procedure which identifies the most significant symbol combinations via a sequential algorithm with Gaussian Approximation (SGA). Simulation results show that our method can approach the optimal a posteriori probability detector (APP) performance while being less complex than comparable suboptimal algorithms, such as the sphere decoder (SD). We further demonstrate that as opposed to the SD the complexity and memory requirements of our algorithm are fixed, therefore easing practical implementation",

keywords = "space-time processing, multiuser detection, Gaussian approximation, probability data association",

author = "Y Jia and C Andrieu and RJ Piechocki and M Sandell",

note = "Publisher: IEEE-Inst Electrical Electronics Engineers Inc Other identifier: IDS Number: 982FG Rose publication type: Journal article Sponsorship: The authors wish to thank Toshiba TREL Bristol UK for sponsoring the work presented in this paper and Dr. Mong Suan Yee for helpful discussions concerning the SD Terms of use: Copyright {\textcopyright} 2005 IEEE. Reprinted from IEEE Communications Letters. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Bristol's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it. ",

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AU - Jia, Y

AU - Andrieu, C

AU - Piechocki, RJ

AU - Sandell, M

N1 - Publisher: IEEE-Inst Electrical Electronics Engineers Inc Other identifier: IDS Number: 982FG Rose publication type: Journal article Sponsorship: The authors wish to thank Toshiba TREL Bristol UK for sponsoring the work presented in this paper and Dr. Mong Suan Yee for helpful discussions concerning the SD Terms of use: Copyright © 2005 IEEE. Reprinted from IEEE Communications Letters. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Bristol's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.

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N2 - The optimal "soft" symbol detection for spatial multiplexing multiple input multiple output (MIMO) system with known channel information requires knowledge of the marginal posterior symbol probabilities for each antenna. The calculation of these quantities requires the evaluation of the likelihood function of the system for all possible symbol combinations, which is prohibitive for large systems. It is however most often the case that most of the transmitted symbol combinations contribute only very little to these marginal posterior probabilities. We propose in this paper a suboptimal procedure which identifies the most significant symbol combinations via a sequential algorithm with Gaussian Approximation (SGA). Simulation results show that our method can approach the optimal a posteriori probability detector (APP) performance while being less complex than comparable suboptimal algorithms, such as the sphere decoder (SD). We further demonstrate that as opposed to the SD the complexity and memory requirements of our algorithm are fixed, therefore easing practical implementation

AB - The optimal "soft" symbol detection for spatial multiplexing multiple input multiple output (MIMO) system with known channel information requires knowledge of the marginal posterior symbol probabilities for each antenna. The calculation of these quantities requires the evaluation of the likelihood function of the system for all possible symbol combinations, which is prohibitive for large systems. It is however most often the case that most of the transmitted symbol combinations contribute only very little to these marginal posterior probabilities. We propose in this paper a suboptimal procedure which identifies the most significant symbol combinations via a sequential algorithm with Gaussian Approximation (SGA). Simulation results show that our method can approach the optimal a posteriori probability detector (APP) performance while being less complex than comparable suboptimal algorithms, such as the sphere decoder (SD). We further demonstrate that as opposed to the SD the complexity and memory requirements of our algorithm are fixed, therefore easing practical implementation

KW - space-time processing

KW - multiuser detection

KW - Gaussian approximation

KW - probability data association

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DO - 10.1109/LCOMM.2005.11018

M3 - Article (Academic Journal)

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SP - 997

EP - 999

JO - IEEE Communications Letters

JF - IEEE Communications Letters

IS - 11

ER -

Gaussian approximation based mixture reduction for near optimum detection in MIMO systems

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