基于神经网络量子算法的多用户检测器

利用遗传量子算法和Hopfield神经网络，提出了一种融合两种算法优点的神经网络量子算法，并将其应用到CDMA通信系统的多用户检测问题中。所提算法把神经网络嵌入到遗传量子算法的每一代中，可进一步提高量子种群的适应度函数值。通过混合神经网络到GQA中，还可加快GQA的收敛速度进而减少算法的计算复杂度。另外，GQA所提供的良好初值改善了HNN的性能，嵌入的HNN也提高了GQA的性能。仿真结果证明了该方法的抗多址干扰能力和抗远近效应能力都优于传统检测器和一些应用智能算法的多用户检测器。

Multiuser Detector Based on Neural Network Quantum Algorithm

A neural network quantum algorithm(NNQA) that employs a genetic quantum algorithm(GQA) and a Hopfield neural network(HNN) for the multiuser detection problem in code-division multiple-access(CDMA) communications system is proposed.Using this algorithm,the Hopfield neural network is embedded into the GQA to improve further the fitness of the population at each generation.Such a hybridization of the GQA with the Hopfield neural network reduces its computational complexity by providing faster convergence.A better initial data estimation supplied by the GQA improves the performance of the HNN,and the embedded Hopfield neural network improves the performance of the GQA.Simulation results show that the proposed approach of multiuser detection has significant performance improvements over conventional detector and the previous detectors based on intelligent algorithms in terms of bit-error-rate,multiple access interference and near-far resistance.