Low-complexity prediction techniques of K-best sphere decoding for MIMO systems

Hsiu Chi Chang, Yen Chin Liao, Hsie-Chia Chang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

In multiple-input multiple output (MIMO) systems, maximum likelihood (ML) detection can provide good performance, however, exhaustively searching for the ML solution becomes infeasible as the number of antenna and constellation points increases. Thus ML detection is often realized by K-best sphere decoding algorithm. In this paper, two techniques to reduce the complexity of K-best algorithm while remaining an error probability similar to that of the ML detection is proposed. By the proposed K-best with predicted candidates approach, the computation complexity can be reduced. Moreover, the proposed adaptive K-best algorithm provides a means to determine the value K according the received signals. The simulation result shows that the reduction in the complexity of 64-best algorithm ranges from 48% to 85%, whereas the corresponding SNR degradation is maintained within 0.13dB and 1.1dB for a 64-QAM 4×4 MIMO system.

Original languageEnglish
Title of host publication2007 IEEE Workshop on Signal Processing Systems, SiPS 2007, Proceedings
Pages45-49
Number of pages5
DOIs
StatePublished - 1 Dec 2007
Event2007 IEEE Workshop on Signal Processing Systems, SiPS 2007 - Shanghai, China
Duration: 17 Oct 200719 Oct 2007

Publication series

NameIEEE Workshop on Signal Processing Systems, SiPS: Design and Implementation
ISSN (Print)1520-6130

Conference

Conference2007 IEEE Workshop on Signal Processing Systems, SiPS 2007
CountryChina
CityShanghai
Period17/10/0719/10/07

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