摘要
蚁群算法是一种生物仿生算法,该算法具有正向反馈、并行计算等特点,可以有效地避免算法收敛到局部最优解。将蚁群算法用于求解盲均衡问题的恒模代价函数,提出一种基于蚁群算法的盲均衡算法,新算法加快了收敛速度,减少了码间干扰的影响,降低了误码率。计算机仿真验证了新算法的有效性。
Ant colony optimization （AC0）, based upon swarm intelligence, is a novel category of bionic meta-heuristic algorithm. Positive feedback mechanism and parallel computation are adopted in this algorithm. Ant colony algorithm was used to avoid the default that the algorithm converges on the part optimum solution. Ant colony algorithm was utilized to optimize the constant module cost function. A bBnd equalization algorithm based on ant colony algorithm was proposed. The novel blind equalization algorithm speeds up the convergence rate, reduces inter-symbol interference and bit error rate. Simulation results illustrate the attractive performance of the proposed algorithm.
出处
《系统仿真学报》
CAS
CSCD
北大核心
2010年第2期395-398,共4页
Journal of System Simulation
基金
中国博士后基金(20060390170)
天津市高等学校科技发展基金(20060610)
关键词
盲均衡
蚁群算法
恒模特性
代价函数
blind equalization
ant colony algorithm
constant module character
cost function
