基于模拟退火粒子群优化的恒模算法

传统的固定步长恒模盲均衡算法在收敛速度和稳态剩余误差之间存在着矛盾,影响了恒模算法的均衡性能。为了解决这一缺点,提高恒模算法的性能,本文在介绍恒模盲均衡原理的基础上,提出一种变步长恒模算法,利用粒子群优化算法对均衡器的权向量进行优化,为避免优化过程进入局部极小值引入了模拟退火算法。通过在短波信道中的仿真,证明了该算法不仅加快了收敛速度,并且具有更高的收敛精度。

Modified Constant Modulus Algorithm based on Simulated Annealing Particle Swarm

The traditional fixed step-size constant modulus blind equalization algorithm has a contradiction in between the convergence rate and steady-state residual error, and this affects the equalization performance of constant modulus algorithm.To solve this problem and improve the performance of constant modulus algorithm, based on the principle of constant modulus blind equalization, a variable step-size constant modulus algorithm is proposed. The weight vector of equalizer is optimized with particle swarm optimization algorithm, and simulated annealing algorithm is introduced into the optimazation process,thus to aviod the access of local minimum. Simulations in the shortwave channel indicate that the proposed algorithm could speed up the convergence rate while enjoying a higher convergence precision.