基于改进的量子遗传算法的多用户检测

为了加快量子遗传算法(QGA)的收敛速度,减小基于QGA的多用户检测的计算复杂度,提出了一种基于改进的量子遗传优化算法(MQGA)的多用户检测算法.通过引入小生境协同进化策略初始化量子种群,并采用自适应更新旋转角策略,改善了QGA的收敛性能和寻优能力.仿真结果表明,提出的多用户检测算法在收敛速度、误码率、抗远近能力及系统容量方面都优于遗传算法多用户检测器、量子遗传算法多用户检测器和传统多用户检测器.

Multi-user Detection Based on Modified Quantum Genetic Algorithm

To speed up the convergence of the quantum genetic algorithm (QGA) and to reduce the computational complexity of the multi-user detector based on QGA,an algorithm that employs modified quantum genetic algorithm in multi-user detection is proposed (MQGA-MUD).Evolutionary strategy with niche is used to initialize the quanta swarm and the adaptive strategy is used to update rotation angle,so that the convergence performance and the searching ability of the QGA are improved further.The simulation results show that the proposed multi-user detection method has better performance than the conventional receivers,and previous multi-user detector based on the genetic algorithm and the quantum genetic algorithm in terms of the convergence rate,bit-error-rate, near-far resistance and capacity of system.