Multiuser Detection Using the Clonal Selection Algorithm and Hopfield Neural Network

In this paper, we present multi-user detection technique based on a novel clonal selection algorithm (CSA) and Hopfield neural network, for code-division multiple-access communications system. Using this approach, the Hopfield neural network is embedded into the CSA as an "immune operator" to improve further the affinity of the antibodies at each generation. Such a hybridization of the CSA with the Hopfield neural network reduces its computational complexity by providing faster convergence. In addition, the embedded Hopfield neural network improves the performance of the CSA. Simulation results are provided to show that the proposed approach of multiuser detection has significant performance improvements over the conventional detector and some detectors based on the previous algorithms in bit-error-rate, multiple access interference and near-far resistance