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NearOptimal Signal Detection Based on the MMSE Detection Using MultiDimensional Search for Correlated MIMO Channels
Liming ZHENG Kazuhiko FUKAWA Hiroshi SUZUKI Satoshi SUYAMA
Publication
IEICE TRANSACTIONS on Communications
Vol.E94B
No.8
pp.23462356 Publication Date: 2011/08/01
Online ISSN: 17451345
DOI: 10.1587/transcom.E94.B.2346
Print ISSN: 09168516 Type of Manuscript: PAPER Category: Wireless Communication Technologies Keyword: correlated MIMO channel, signal detection, low complexity, MMSE, noise enhancement, multidimensional search,
Full Text: FreePDF(1.1MB)
Summary:
This paper proposes a lowcomplexity signal detection algorithm for spatially correlated multipleinput multipleoutput (MIMO) channels. The proposed algorithm sets a minimum meansquare error (MMSE) detection result to the starting point, and searches for signal candidates in multidimensions of the noise enhancement from which the MMSE detection suffers. The multidimensional search is needed because the number of dominant directions of the noise enhancement is likely to be more than one over the correlated MIMO channels. To reduce the computational complexity of the multidimensional search, the proposed algorithm limits the number of signal candidates to O(N_{T}) where N_{T} is the number of transmit antennas and O( ) is big O notation. Specifically, the signal candidates, which are unquantized, are obtained as the solution of a minimization problem under a constraint that a stream of the candidates should be equal to a constellation point. Finally, the detected signal is selected from hard decisions of both the MMSE detection result and unquantized signal candidates on the basis of the log likelihood function. For reducing the complexity of this process, the proposed algorithm decreases the number of calculations of the log likelihood functions for the quantized signal candidates. Computer simulations under a correlated MIMO channel condition demonstrate that the proposed scheme provides an excellent tradeoff between BER performance and complexity, and that it is superior to conventional onedimensional search algorithms in BER performance while requiring less complexity than the conventional algorithms.

