A best-first tree-searching approach for ML decoding in MIMO system

Chung-An Shen; Ahmed M. Eltawil; Sudip Mondal; Khaled N. Salama

doi:10.1109/ISCAS.2010.5537825

A best-first tree-searching approach for ML decoding in MIMO system

Chung-An Shen, Ahmed M. Eltawil, Sudip Mondal, Khaled N. Salama

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

Abstract

In MIMO communication systems maximum-likelihood (ML) decoding can be formulated as a tree-searching problem. This paper presents a tree-searching approach that combines the features of classical depth-first and breadth-first approaches to achieve close to ML performance while minimizing the number of visited nodes. A detailed outline of the algorithm is given, including the required storage. The effects of storage size on BER performance and complexity in terms of search space are also studied. Our result demonstrates that with a proper choice of storage size the proposed method visits 40% fewer nodes than a sphere decoding algorithm at signal to noise ratio (SNR) = 20dB and by an order of magnitude at 0 dB SNR.

Original language	English (US)
Title of host publication	Proceedings of 2010 IEEE International Symposium on Circuits and Systems
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	3533-3536
Number of pages	4
ISBN (Print)	9781424453085
DOIs	https://doi.org/10.1109/ISCAS.2010.5537825
State	Published - Aug 9 2010

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10.1109/ISCAS.2010.5537825

https://repository.kaust.edu.sa/bitstream/10754/236095/1/ABest-FirstTree-SearchingApproachforMLDecodinginMIMOSystem.pdf

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@inproceedings{3aa45bf5ce5f47029d7e588da0643c60,

title = "A best-first tree-searching approach for ML decoding in MIMO system",

abstract = "In MIMO communication systems maximum-likelihood (ML) decoding can be formulated as a tree-searching problem. This paper presents a tree-searching approach that combines the features of classical depth-first and breadth-first approaches to achieve close to ML performance while minimizing the number of visited nodes. A detailed outline of the algorithm is given, including the required storage. The effects of storage size on BER performance and complexity in terms of search space are also studied. Our result demonstrates that with a proper choice of storage size the proposed method visits 40% fewer nodes than a sphere decoding algorithm at signal to noise ratio (SNR) = 20dB and by an order of magnitude at 0 dB SNR.",

author = "Chung-An Shen and Eltawil, {Ahmed M.} and Sudip Mondal and Salama, {Khaled N.}",

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T1 - A best-first tree-searching approach for ML decoding in MIMO system

AU - Shen, Chung-An

AU - Eltawil, Ahmed M.

AU - Mondal, Sudip

AU - Salama, Khaled N.

N1 - KAUST Repository Item: Exported on 2020-10-01

PY - 2010/8/9

Y1 - 2010/8/9

N2 - In MIMO communication systems maximum-likelihood (ML) decoding can be formulated as a tree-searching problem. This paper presents a tree-searching approach that combines the features of classical depth-first and breadth-first approaches to achieve close to ML performance while minimizing the number of visited nodes. A detailed outline of the algorithm is given, including the required storage. The effects of storage size on BER performance and complexity in terms of search space are also studied. Our result demonstrates that with a proper choice of storage size the proposed method visits 40% fewer nodes than a sphere decoding algorithm at signal to noise ratio (SNR) = 20dB and by an order of magnitude at 0 dB SNR.

AB - In MIMO communication systems maximum-likelihood (ML) decoding can be formulated as a tree-searching problem. This paper presents a tree-searching approach that combines the features of classical depth-first and breadth-first approaches to achieve close to ML performance while minimizing the number of visited nodes. A detailed outline of the algorithm is given, including the required storage. The effects of storage size on BER performance and complexity in terms of search space are also studied. Our result demonstrates that with a proper choice of storage size the proposed method visits 40% fewer nodes than a sphere decoding algorithm at signal to noise ratio (SNR) = 20dB and by an order of magnitude at 0 dB SNR.

UR - http://hdl.handle.net/10754/236095

UR - http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5537825

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U2 - 10.1109/ISCAS.2010.5537825

DO - 10.1109/ISCAS.2010.5537825

M3 - Conference contribution

SN - 9781424453085

SP - 3533

EP - 3536

BT - Proceedings of 2010 IEEE International Symposium on Circuits and Systems

PB - Institute of Electrical and Electronics Engineers (IEEE)

ER -

A best-first tree-searching approach for ML decoding in MIMO system

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