A Hybrid ML Decoding Scheme for Multiple Input Multiple Output Signals on Partitioned Tree

In this paper, we propose a novel ML decoding scheme based on the combination of depth- and breadth-first search methods on a partitioned tree for multiple input multiple output (MIMO) systems. The proposed scheme first partitions the searching tree into several stages, each of which is then searche...

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Bibliographic Details
Published in2008 IEEE 68th Vehicular Technology Conference pp. 1 - 5
Main Authors Jongho Oh, Iickho Song, Juho Park, Jeong, M.A., Myeong Soo Choi
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2008
Subjects
Bit error rate
Complexity theory
Decision feedback equalizers
Decoding
MIMO
Search methods
Signal to noise ratio
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Summary:In this paper, we propose a novel ML decoding scheme based on the combination of depth- and breadth-first search methods on a partitioned tree for multiple input multiple output (MIMO) systems. The proposed scheme first partitions the searching tree into several stages, each of which is then searched by a depth- or breadth-first search method, maximally exploiting the advantages of both the depth- and breadth-first search methods. Numerical results indicate that, when the depth- and breadth-first search algorithms are adopted appropriately, the proposed scheme exhibits substantially lower computational complexity than conventional ML decoders while maintaining the ML bit error performance.
ISBN:142441721X
9781424417216
ISSN:1090-3038
2577-2465
DOI:10.1109/VETECF.2008.94