Title :
Soft-Information Assisted Near-Optimum Nonlinear Detection for BLAST-type Space Division Multiplexing OFDM Systems
Author :
Jiang, M. ; Akhtman, J. ; Hanzo, Lajos
Author_Institution :
Sch. of Electron. & Comput. Sci., Southampton Univ.
fDate :
4/1/2007 12:00:00 AM
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 scheme 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; convolutional codes; genetic algorithms; maximum likelihood detection; space division multiplexing; turbo codes; BLAST; nonlinear hybrid detection scheme; orthogonal frequency division multiplexing; receiver antennas; soft-information assisted genetic algorithm; soft-information assisted maximum likelihood detection; soft-information assisted near-optimum nonlinear detection; space division multiplexing OFDM systems; transmit antennas; turbo convolutional codes; Convolutional codes; Frequency division multiplexing; Genetic algorithms; MIMO; Maximum likelihood detection; Multiaccess communication; Multiuser detection; OFDM; Receiving antennas; Transmitting antennas;
Journal_Title :
Wireless Communications, IEEE Transactions on
DOI :
10.1109/TWC.2007.348318