DocumentCode
1694812
Title
Detection Algorithm for V-BLAST System Based on Hybrid Discrete Particle Swarm Optimization
Author
Dong, Wei ; Li, Jiandong ; Zhu, Mingming ; Chen, Liang
Author_Institution
State Key Lab. of Integrated Service Networks, Xidian Univ., Xian
fYear
2008
Firstpage
196
Lastpage
200
Abstract
According, to the new discrete particle swarm optimization (NDPSO) and combining NDPSO with the mutation operator of the genetic algorithm, we proposed a novel hybrid discrete particle swarm optimization detection algorithm for the vertical bell-labs layered space-time (V-BLAST) system. The complexity of the proposed detection algorithm is analyzed. Compared with the maximum likelihood (ML) detection algorithm which has the best BER performance but the highest computational complexity, the proposed detection algorithm is capable of reducing the computational complexity significantly. Simulation results show that the bit error rate (BER) of the proposed algorithm is very close to that of the ML detection algorithm and significantly lower than the other suboptimal detection algorithms. The algorithm is a new method to solve the detection problem in V-BLAST system.
Keywords
computational complexity; error statistics; genetic algorithms; maximum likelihood detection; particle swarm optimisation; space-time codes; BER performance; V-BLAST system; bit error rate; computational complexity; genetic algorithm; hybrid discrete particle swarm optimization; maximum likelihood detection algorithm; mutation operator; new discrete particle swarm optimization; suboptimal detection algorithms; vertical bell-labs layered space-time system; Algorithm design and analysis; Bit error rate; Computational complexity; Computational modeling; Detection algorithms; Laboratories; Maximum likelihood detection; Particle swarm optimization; Receiving antennas; Transmitting antennas;
fLanguage
English
Publisher
ieee
Conference_Titel
Circuits and Systems for Communications, 2008. ICCSC 2008. 4th IEEE International Conference on
Conference_Location
Shanghai
Print_ISBN
978-1-4244-1707-0
Electronic_ISBN
978-1-4244-1708-7
Type
conf
DOI
10.1109/ICCSC.2008.48
Filename
4536740
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