Quantum Particle Swarm Optimization for MC-CDMA Multiuser Detection

To resolve local convergent problem of the standard discrete particle swarm optimization algorithm, a novel quantum particle swarm optimization (QPSO) algorithm that use new move equation is proposed. The proposed algorithm is based on quantum velocity and quantum evolution mechanism with particle evolution principle. The quantum particle swarm optimization algorithm is used to solve multiuser detection problem of multi-carrier code division multiple access (MC-CDMA) system. By hybridizing the Hopfield neural network and quantum evolutionary, quantum velocity and measure state can be co-evolutionary. The new algorithm can search global optimal solution in faster convergence rate. Simulation results for synchronous MC-CDMA system are provided to show that the designed detector is superior to the conventional detector and some previous detectors in bit error rate (BER), multiple access interference and near-far resistance.