NDLI: An efficient quasi-maximum likelihood decoder for PSK signals

Content Provider	IEEE Xplore Digital Library
Author	Zhi-Quan Luo Xiaodong Luo Kisialiou, M.
Copyright Year	2003
Description	Author affiliation: McMaster Univ., Hamilton, Ont., Canada (Zhi-Quan Luo)
Abstract	Since exact maximum likelihood (ML) detection is computationally intractable in general, approximate ML approaches are needed to reduce the computation time while maintaining low bit error rate (BER). In this work, we develop an efficient approximate ML decoder for constant modulus signals based on a simple nonlinear programming relaxation. Unlike the existing sphere decoder whose expected complexity is cubic in problem size and whose performance deteriorates with increasing problem size and noise level, our proposed new decoder enjoys a worst case quadratic complexity and scales gracefully with problem dimension and noise level. Our initial testing and analysis suggests that this new decoder is capable of delivering ML like BER performance for PSK signals while requiring substantially lower computational complexity. In this sense, our new decoder is similar to the sphere decoder which is an effective method for QAM signals.
Sponsorship	IEEE Signal Process, Soc
File Size	256075
File Format	PDF
ISBN	0780376633
ISSN	15206149
DOI	10.1109/ICASSP.2003.1201743
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-04-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Phase shift keying Maximum likelihood decoding Bit error rate Maximum likelihood detection Noise level Testing Signal analysis Performance analysis Computational complexity Quadrature amplitude modulation
Content Type	Text
Resource Type	Article

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