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Intelligent multi-user detection using an artificial immune system

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Abstract

Artificial immune systems (AIS) are a kind of new computational intelligence methods which draw inspiration from the human immune system. In this study, we introduce an AIS-based optimization algorithm, called clonal selection algorithm, to solve the multi-user detection problem in code-division multiple-access communications system based on the maximum-likelihood decision rule. Through proportional cloning, hypermutation, clonal selection and clonal death, the new method performs a greedy search which reproduces individuals and selects their improved maturated progenies after the affinity maturation process. Theoretical analysis indicates that the clonal selection algorithm is suitable for solving the multi-user detection problem. Computer simulations show that the proposed approach outperforms some other approaches including two genetic algorithm-based detectors and the matched filters detector, and has the ability to find the most likely combinations.

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Correspondence to MaoGuo Gong.

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Supported by the National Natural Science Foundation of China (Grant Nos. 60703107, 60703108), the National High-Tech Research & Development Program of China (Grant No. 2009AA12Z210), the Program for New Century Excellent Talents in University (Grant No. NCET-08-0811), and the Program for Cheung Kong Scholars and Innovative Research Team in University (Grant No. IRT-06-45)

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Gong, M., Jiao, L., Ma, W. et al. Intelligent multi-user detection using an artificial immune system. Sci. China Ser. F-Inf. Sci. 52, 2342–2353 (2009). https://doi.org/10.1007/s11432-009-0201-y

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  • DOI: https://doi.org/10.1007/s11432-009-0201-y

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