Multiuser Detection Using Immune Ant Colony Optimization

To design optimal multiuser detector of low complexity, a simple ant colony optimization algorithm (ACO) is presented. By introducing the information processing mechanism of artificial immune system and neural network to ACO, an immune ant colony optimization (IACO) is proposed. In IACO, a new method of preparing vaccine using Hopfield neural network is presented to form immune operator. The proposed IACO is a hybridization method of the ACO with the immune operator that reduces the computational complexity by providing faster convergence and improves the performance of ACO. Then a novel multiuser detector based on IACO is designed in CDMA system. Simulation results show that the proposed detector is superior to the multiuser detectors based on the previous intelligent algorithms in bit error rate, and achieve the global optimization solution in fast convergence rate.

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