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Author Jun Won Choi ♦ Byonghyo Shim ♦ Singer, A.C. ♦ Nam Ik Cho
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2008
Language English
Subject Domain (in DDC) Technology ♦ Engineering & allied operations ♦ Applied physics
Subject Keyword Maximum likelihood decoding ♦ Complexity theory ♦ Lattices ♦ Noise ♦ Signal to noise ratio ♦ Decoding ♦ Bit error rate ♦ Sphere decoding ♦ Maximum likelihood ♦ Dimension reduction ♦ MIMO ♦ Tree search
Abstract In this paper, we propose a near maximum likelihood (ML) decoding technique, which reduces the computational complexity of the exact ML decoding algorithm. The computations needed for the tree search in the ML decoding is simplified by reducing the dimension of the search space prior to the tree search. In order to compensate performance loss due to the dimension reduction, a list stack algorithm (LSA) is considered, which produces a list of the top K closest points. The combination of both approaches, called reduced dimension list stack algorithm (RD-LSA), is shown to provide flexibility and offers a performance-complexity trade-off. Simulations performed for V-BLAST transmission demonstrate that significant complexity reduction can be achieved compared to the sphere decoding algorithm (SDA) while keeping the performance loss below an acceptable level.
Description Author affiliation: Coordinated Sci. Lab., Univ. of Illinois at Urbana-Champaign, Urbana, IL (Jun Won Choi)
ISBN 9781424422401
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2008-07-21
Publisher Place Germany
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 1.33 MB
Page Count 4
Starting Page 41
Ending Page 44

Source: IEEE Xplore Digital Library