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

Content Provider	IEEE Xplore Digital Library
Author	Chung-An Shen Eltawil, A.M. Mondal, S. Salama, K.N.
Copyright Year	2010
Description	Author affiliation: EECS Department, University of California, Irvine, CA, USA (Chung-An Shen; Eltawil, A.M.) \|\| Cypress Semiconductors Corporation, San Jose, CA, USA (Mondal, S.) \|\| EE Program, King Abdullah University of Science and Technology, Thuwal, Kingdom of Saudi Arabia (Salama, K.N.)
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.
Starting Page	3533
Ending Page	3536
File Size	262600
Page Count	4
File Format	PDF
ISBN	9781424453085
DOI	10.1109/ISCAS.2010.5537825
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-05-30
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	MIMO Maximum likelihood decoding Bit error rate Signal to noise ratio Throughput Receiving antennas Matrix decomposition Degradation Transmitting antennas Constellation diagram
Content Type	Text
Resource Type	Article

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