基于概率和自适应的最大后验概率盲均衡算法

MAP blind equalization algorithm based on probability and adaptive

为了减小最大后验概率(maximum a posteriori,MAP)盲均衡算法中稳态剩余误差和加快收敛速度,提出了一种改进的基于概率和自适应σ的MAP盲均衡算法。根据概率论的原理,分析了MAP算法中σ表示的实际意义,改进算法通过改变更新方程中的高斯信号方差值σ,即由固定值改为可变值,再运用不等概思想进一步的提高了MAP算法的收敛性能。仿真结果表明,与MAP盲均衡算法相比,改进型MAP盲均衡算法具有更好的均衡效果和收敛性能。

To reduce the residual mean square error （MSE） and increase the convergence speed in MAP, an improved MAP blind equalization algorithm based on probability and adaptive is presented. According to the principles of probability theory, the prac- tical significance of the o in the MAP algorithm is analyzed. The proposed algorithm changes the Gaussian variance o in renewal equation from fixed value to variable value, which further improves the convergence of MAP blind equalization algorithm using the idea of not equal to the probability. The simulation results show that the equalization effect and convergence performance of modified MAP blind equalization algorithm are better than those of MAP blind equalization algorithra.