稀疏多径信道自适应均衡算法研究

针对传统自适应均衡算法在稀疏多径信道中性能较差的问题，提出了一种基于l₂-范数的自适应均衡算法。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，以解决迭代参数的设置及收敛速度慢的问题。该算法将l₂-范数和压缩感知相结合，不仅提高了权值的精度，而且降低了计算复杂度。仿真结果表明，该算法计算量小，训练序列少，具有较好的性能，对提高系统的通信性能具有参考价值。

Research on adaptive equalization algorithm for sparse multipath channel

In order to solve the problem of poor performance of traditional adaptive equalization algorithm in sparse multipath channels, a new adaptive equalization algorithm based on l₂ -norm is proposed. This algorithm takes advantage of the sparsity of equalizer weights in sparse multipath channel, and regards the training process of adaptive equalizer as the weighted sum of sparse signal to dictionary in compressed sensing theory, so as to solve the problems of iterative parameter setting and slow convergence. This new algorithm, which combines l₂ -norm with compressed sensing, not only improves the weight accuracy, but also reduces the computational complexity. Simulation results show that the proposed algorithm can achieve better performance with less computation amount and fewer training sequences, and has a reference value for improving the communication performance of the system.