Immune clonal selection algorithm for multiuser detection in DS-CDMA systems
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
A novel genetic algorithm based on immunity
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Hopfield neural network implementation of the optimal CDMA multiuser detector
IEEE Transactions on Neural Networks
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Based on the Antibody Clonal Selection Theory of immunology, we put forward a novel clonal selection algorithm for multiuser detection in Code-division Multiple-access Systems. By using the clonal selection operator, the new algorithm can carry out the global search and the local search in many directions rather than one direction around the same individual simultaneously. After discussing the main characters of the new algorithm, especially the convergence and complexity, the performance of the proposed receiver, named by CAMUD, is evaluated via computer simulations and compared to that of other suboptimal schemes as well as to that of Optimal Multiuser detector (OMD) and conventional detector in CDMA systems over Multi-Path Channels. When compared with the OMD scheme, the CAMUD is capable of reducing the computational complexity significantly. On the other hand, when compared with standard genetic algorithm and improved genetic algorithm, theoretical analysis and Monte Carlo simulations show that the CAMUD with same complexity has optimal performance in eliminating MAI and "near-far" resistance. The simulations also show that the CAMUD performs quite well even when the number of active users and the length of the transmitted packet are considerably large.