DocumentCode :
2474181
Title :
First Look at Average-Case Complexity for Planar Maximum-Likelihood Detection
Author :
Wong, Kai-Kit ; Mamoulis, Nikos
Author_Institution :
Centre for Commun. Syst. & Technol., Hull Univ.
fYear :
0
fDate :
0-0 0
Firstpage :
203
Lastpage :
207
Abstract :
In this paper, an efficient exact maximum-likelihood (ML) detection scheme is presented for a multiple-input single-output (MISO) system with real signal constellations. The proposed technique has a geometrical interpretation of exploring the points jointly "close" in all coordinate axes around the decoding hyperplane and is therefore dubbed planar detection. The fact that the lattice points which are close in all coordinate axes are much less, leads to dramatic reduction in detection complexity. Making a few approximations, this paper derives the average-case complexity exponent, ec, for planar detection analytically in a closed form. Numerical results show that for an (n, 1) system, although the expected complexity is still exponential, complexity reduction of 2 exponents, i.e., from ec to ec -2, is realized and such advantage is promised irrespective of the size of the signal constellations and the received signal-to-noise ratio (SNR)
Keywords :
maximum likelihood decoding; maximum likelihood detection; MISO system; average-case complexity; decoding hyperplane; geometrical interpretation; multiple-input single-output; planar maximum-likelihood detection; signal constellation; Constellation diagram; Degradation; Lattices; MIMO; Maximum likelihood decoding; Maximum likelihood detection; Multiuser detection; Pulse modulation; Search problems; Signal to noise ratio;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information, Communications and Signal Processing, 2005 Fifth International Conference on
Conference_Location :
Bangkok
Print_ISBN :
0-7803-9283-3
Type :
conf
DOI :
10.1109/ICICS.2005.1689035
Filename :
1689035
Link To Document :
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