一种适用于稀疏多径信道的自适应均衡算法

针对传统的自适应均衡算法在稀疏多径信道下性能表现不佳的问题，提出了一种基于基追踪降噪的自适应均衡算法。该算法利用稀疏多径信道下均衡器权值的稀疏性，将自适应均衡器的训练过程看作压缩感知理论中稀疏信号对字典的加权求和，并利用重构算法直接对稀疏权值进行求解，解决了迭代参数设置和收敛慢的问题。采用基追踪降噪作为重构算法并选用变量分离近似稀疏重构对该最优化问题进行求解，既提高了权值的重构精度又降低了计算的复杂度。仿真结果表明，所提算法能够以较低的计算量和较少的训练序列达到更优性能，这对提升系统的通信性能具有参考价值。

An adaptive equalization algorithm for sparse multipath channel

For the poor performance of the traditional adaptive equalization algorithms for sparse multipath channel,an adaptive equalization algorithm based on basis pursuit de-noising is proposed.This algorithm uses sparseness of the weights of the adaptive equalizer in sparse multipath channel,and the adaptive equalizer training process can be modeled as a weighted summation of a dictionary by the sparse source based on compressed sensing theory.And the sparse weights can be achieved by the reconfiguring algorithm,so the problem of iterative parameter settings and convergence can be solved.In order to improve the accuracy of weight recovery and reduce the computational complexity,basis pursuit de-noising algorithm is chosen as the reconfiguring algorithm and sparse reconstruction by separable approximation is used to solve the optimization problem.The simulation shows that the proposed algorithm can achieve better performance with lower calculation amount and less training sequences,which has reference value for improving the communication performance of the system in sparse multipath channel.