【摘要】 为解决在正交小波CMA盲均衡(WBCMA)中,收敛速度与计算量相对矛盾的问题,这里引入了一种最新的基于Renyi熵的随机梯度最陡下降的盲均衡算法.与以往的CBA算法相比,该算法不仅使得正交小波盲均衡算法的收敛速度显著增加,而且计算量也增加很小.仿真结果证明了此方法的优越性.另外对新算法的参数,α,N的各种情况进行了研究讨论,仿真表明当α=2,N=2时,能取得更好的效果.更多还原

【Abstract】 To solve the contradictory problem of convergence speed and computational cost in orthonomal wavelet based CMA blind equalization, a novel blind equalization algorithm was formulated which is based on stochastic gradient decent minimization of Renyi’s entropy. Compared with other CMA equalizers, the algorithm results in a remarkable increase in convergence speed, but only a moderate increase in computational cost. Simulation for almost every situation of parameters (α,N) was performed to validate superiority of this method.更多还原