DocumentCode
538933
Title
Multiuser Detection Based on the DNA Clonal Selection Algorithm
Author
Gao, Hongyuan ; Cao, Jinlong ; Yu, Xuemei
Author_Institution
Coll. of Inf. & Commun. Eng., Harbin Eng. Univ., Harbin, China
Volume
2
fYear
2010
fDate
16-17 Dec. 2010
Firstpage
349
Lastpage
352
Abstract
In this paper, a novel multi-user detection based on a DNA clonal selection algorithm (DNACSA) is proposed for code-division multiple-access communications system. To design an efficient DNACSA based detector, the stochastic Hop field neural network is embedded into the DNACSA as an “immune operator” to improve further the affinity of the DNA antibodies at each generation. Such a hybridization of the DNACSA with the stochastic Hop field neural network reduces its computational complexity by providing faster convergence. In addition, the embedded Hop field neural network improves the performance of the DNACSA. Simulation results are provided to show that the proposed DNACSA detector has significant performance improvements over the conventional detectors and some detectors based on the previous intelligence algorithms in bit-error-rate and multiple access interference resistance.
Keywords
DNA; Hopfield neural nets; biocomputing; code division multiple access; computational complexity; convergence; genetic algorithms; multiuser detection; DNA antibody; DNA clonal selection algorithm; DNACSA based detector; bit error rate; code division multiple access communication system; computational complexity; immune operator; intelligence algorithm; multiple access interference resistance; multiuser detection; stochastic Hopfield neural network; DNA; Detectors; Gallium; Hopfield neural networks; Multiaccess communication; Multiuser detection; Optimization; DNA computation; clonal selection algorithm; code division multiple access (CDMA); multiuser detection; neural network;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Systems (GCIS), 2010 Second WRI Global Congress on
Conference_Location
Wuhan
Print_ISBN
978-1-4244-9247-3
Type
conf
DOI
10.1109/GCIS.2010.218
Filename
5709283
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