DocumentCode :
2576217
Title :
Near-optimum nonlinear soft detection for multiple-antenna assisted OFDM
Author :
Jiang, M. ; Akhtman, J. ; Hanzo, L.
Author_Institution :
Sch. of ECS, Southampton Univ.
Volume :
4
fYear :
2006
fDate :
3-6 April 2006
Firstpage :
1989
Lastpage :
1993
Abstract :
In this contribution, a nonlinear hybrid detection scheme based on a novel soft-information assisted genetic algorithm (GA) is proposed for a turbo convolutional (TC) coded space division multiplexing (SDM) aided orthogonal frequency division multiplexing (OFDM) system. Our numerical results show that the performance of the currently known GA-assisted system can be improved by about 2 dB with the aid of the GA´s population-based soft solution, approaching the optimum performance of the soft-information assisted maximum likelihood (ML) detection, while exhibiting a lower complexity, especially in high-throughput scenarios. Furthermore, the proposed technique is capable of achieving a good performance even in the so-called overloaded systems, where the number of transmit antennas is higher than the number of receiver antennas
Keywords :
OFDM modulation; antenna arrays; convolutional codes; genetic algorithms; maximum likelihood detection; space division multiplexing; turbo codes; genetic algorithm; multiple-antenna assisted OFDM; near-optimum nonlinear soft detection; nonlinear hybrid detection scheme; orthogonal frequency division multiplexing; receiver antennas; soft-information assisted maximum likelihood detection; space division multiplexing; transmit antennas; turbo convolutional code; Convolutional codes; Genetic algorithms; MIMO; Maximum likelihood detection; Multiaccess communication; Multiuser detection; OFDM; Receiving antennas; Transmitting antennas; Writing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Wireless Communications and Networking Conference, 2006. WCNC 2006. IEEE
Conference_Location :
Las Vegas, NV
ISSN :
1525-3511
Print_ISBN :
1-4244-0269-7
Electronic_ISBN :
1525-3511
Type :
conf
DOI :
10.1109/WCNC.2006.1696601
Filename :
1696601
Link To Document :
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