摘要
为提高高阶非常模信号的均衡效果,提出了量子人工鱼群优化的自适应最小熵盲均衡算法。该算法利用超指数迭代加快了自适应最小熵盲均衡算法的收敛速度;利用薛定谔方程设计量子粒子群模型的思想,设计了快速全局寻优能力的量子人工鱼群模型,减小了超指数自适应最小熵盲均衡算法的稳态误差。仿真结果表明:与自适应最小熵盲均衡算法、超指数自适应最小熵盲均衡算法相比,量子人工鱼群优化的自适应最小熵盲均衡算法收敛速度快、稳态误差小,有利于提高通信质量。
In order to improve the equalization performance of high order inconstant modulus signals, adaptive minimum entropy super-exponential iteration blind equalization algorithm based on quantum artificial fish swarm optimization was proposed. The proposed algorithm could accelerate convergence rate via super-exponential iteration algorithm and could further decease the mean square error of the super-exponential iteration adaptive minimum entropy blind equalization algorithm via using the global optimization of the quantum artificial fish swarm algorithm designed by Schrodinger equation. The simulation results demonstrate that the proposed algorithm has fast convergence rate and lower mean square error for different higher modulation signals comparison with adaptive minimum entropy blind equalization algorithm and super-exponential iteration adaptive minimum entropy blind equalization algorithm.
出处
《系统仿真学报》
CAS
CSCD
北大核心
2016年第2期449-454,共6页
Journal of System Simulation
基金
江苏省高校科研成果产业化推进项目(JHB2012-9)
江苏省高校自然科学研究重大项目(13KJA510001)
关键词
盲均衡算法
幅度相位频移键控
收敛速度
量子人工鱼群
blind equalization algorithm
amplitude phase shift key
convergence rate
quantum artificial fish swarm
