A best-first tree-searching approach for ML decoding in MIMO system
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program
Physical Science and Engineering (PSE) Division
Online Publication Date2010-08-09
Print Publication Date2010-05
Permanent link to this recordhttp://hdl.handle.net/10754/236095
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AbstractIn 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.
CitationShen C-A, Eltawil AM, Mondal S, Salama KN (2010) A best-first tree-searching approach for ML decoding in MIMO system. Proceedings of 2010 IEEE International Symposium on Circuits and Systems. doi:10.1109/ISCAS.2010.5537825.
Conference/Event name2010 IEEE International Symposium on Circuits and Systems: Nano-Bio Circuit Fabrics and Systems, ISCAS 2010