基于概率密度函数匹配与分数低阶矩的并行盲均衡算法

Concurrent Blind Equalization Algorithm Based on Probability Density Function Matching and Fractional Lower Order Moments

为了提高脉冲噪声下盲均衡器的性能,该文提出一种基于概率密度函数匹配与分数低阶矩的并行盲均衡算法。首先采用概率密度函数匹配的思想进行盲均衡,充分利用其收敛速度快的优势。为了解决此均衡过程中引起的相位信息损失以及抑制脉冲噪声能力差的问题,又以并行的方式结合判决信号的分数低阶矩,并以此作为代价函数来共同更新盲均衡器的权向量,进一步提高了算法在脉冲噪声下的收敛速度与收敛精度。仿真实验表明,所提算法在有效解决相位旋转问题的同时较好地抑制了脉冲噪声,此外还具有较快的收敛速度和较小的稳态误差,稳健性较强。

In order to improve the performance of the blind equalizer under impulsive noise environments, a novel concurrent blind equalization algorithm based on probability density function matching and fractional lower order moments is presented. This algorithm uses the idea of probability density function matching at the beginning, and makes full use of the advantage of its fast convergence speed. In order to solve the problems of the phase information loss and incapability of suppressing impulse noise, this paper combines the fractional lower order moments of the decision signal in parallel as the cost function to update the weight coefficients of the blind equalizer. The convergence speed and convergence precision is further improved. The simulation experiments results show that the algorithm can effectively solve the problem of phase rotation and better suppress the impulse noise. Moreover, the algorithm has fast convergence speed, small steady-state error and strong robustness.