article · 2010

Multiuser Detection Based on the DNA Clonal Selection Algorithm

Cited by 0Abstract and details only

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.

Cite this paper

APA 7

Gao, H., Cao, J., & Yu, X. (2010). Multiuser Detection Based on the DNA Clonal Selection Algorithm. https://doi.org/10.1109/gcis.2010.218

MLA 9

Gao, Hongyuan, et al. “Multiuser Detection Based on the DNA Clonal Selection Algorithm.” vol. 2, 2010, pp. 349–52, https://doi.org/10.1109/gcis.2010.218.

Chicago

Gao, Hongyuan, Jinlong Cao, and Xuemei Yu. 2010. “Multiuser Detection Based on the DNA Clonal Selection Algorithm.” https://doi.org/10.1109/gcis.2010.218.

Harvard

Gao, H., Cao, J. and Yu, X. (2010) ‘Multiuser Detection Based on the DNA Clonal Selection Algorithm’. Available at: https://doi.org/10.1109/gcis.2010.218.

BibTeX

@article{gao2010multiuser,
  title = {{Multiuser Detection Based on the DNA Clonal Selection Algorithm}},
  author = {Gao, Hongyuan and Cao, Jinlong and Yu, Xuemei},
  volume = {2},
  pages = {349--352},
  year = {2010},
  doi = {10.1109/gcis.2010.218}
}

Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.