引入梯度导引似p范数约束的稀疏信道估计算法

Estimation algorithm for sparse channels with gradient guided p-norm like constraints

为克服l0和l1范数约束的最小均方算法在不同信道稀疏程度下对稀疏信道估计中出现的收敛性能起伏较大等缺点,提出一种新的似p范数约束的最小均方算法,通过在最小均方算法代价函数中引入p值可变的似p范数约束以适应信道的不同稀疏程度,并在验证代价函数凸性的基础上导出p值的梯度导引寻优。最后给出仿真实验及其讨论,实验结果表明了新算法的优越性。

The 10 and II norm constrained least mean square （LMS） algorithm can effectively improve the performance of the sparse channel estimation, but the convergence performance of such algorithms will considerably vary when the channel exhibits different sparisity. A novel p-norm like constraint LMS algorithm to accommodate the various sparisity of the channels through the introducing of the variable p-value was presented. Furthermore, the gradient guided optimization of the p-value was derived. Numerical simulation results are given to demonstrate the superiority of the new algorithm.