基于自适应人工鱼群算法的多用户检测器

将智能优化算法应用到多用户检测器(MUD)问题中,是近年来改善MUD性能的一个研究方向.人工鱼群算法(AFSA)是一种新的智能优化算法,该算法具有一些遗传算法和粒子群算法不具备的特点.但是用其解决离散优化问题时,该算法保持探索与开发平衡的能力较差,且在算法运行后期搜索的盲目性较大,从而影响了该算法搜索的质量和效率.为了克服这些缺点,本文对该算法进行了改进,得到两种自适应人工鱼群算法(AAFSA_FP和AAFSA_SP),并首次用其构建了新的多用户检测器.仿真结果表明,该方法与基于遗传算法的多用户检测器和基于粒子群算法的多用户检测器相比,在误码率、抗远近效应的能力和收敛速度等方面都有明显的改善.

Multiuser Detector Based on Adaptive Artificial Fish School Algorithm

Artificial Fish School Algorithm (AFSA) is a new kind of intelligence optimization algorithm, which has some advantages that Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) do not have. But this algorithm has several disadvantages such as the blindness of searching at the later stage and the poor ability to keep the balance of exploration and exploitation, which reduce its probability of searching the best result. To overcome these problems, two improved AFSA named AAFSA_FP and AAFSA_SP were proposed based on idea of adaptive. Then the new algorithms are applied to solve the multiuser detection problems. Simulation results show that the proposed detectors outperform GA detector and PSO detector in terms of BER, near-far resistant and convergence performance.