基于基追踪去噪的水声正交频分复用稀疏信道估计

针对传统的l₂-范数信道估计精度低的问题, 提出了一种基于基追踪去噪(BPDN)的水声正交频分复用稀疏信道估计方法, 该方法针对水声信道的稀疏特性, 利用少量的观测值即可以很高的精度估计出信道冲激响应. 与贪婪追踪类算法相比, 基于BPDN算法的稀疏信号估计具有全局最优解, 采用l₂-l₁范数准则估计信号, 同时考虑了观测值含噪情况, 通过调整正则化参数控制估计信号稀疏度和残余误差之间的平衡. 仿真分析了导频分布、正则化参数等对BPDN 算法的影响以及BPDN算法与最小平方(LS)、正交匹配追踪(OMP)信道估计算法的性能. 湖试结果表明, 在稀疏信道下, 基于BPDN的信道估计方法明显优于LS和OMP信道估计方法.

Sparse channel estimation of underwater acoustic orthogonal frequency division multiplexing based on basis pursuit denoising

To solve the problem of poor performance of the traditional l2-norm channel estimation, a sparse channel estimation approach based on basis pursuit denoising (BPDN) is proposed in orthogonal frequency division multiplex underwater acoustic communication. Owing to the sparsity of the underwater acoustic channel, only a few observations are needed to recover the channel impulse response with a high accuracy. Compared with greedy pursuit algorithm, BPDN algorithm has the globally excellentest solution. The signal is estimated based on the l₂-l₁ norm rule and the observations containing the noise are considered. The regularization parameter can be changed to balance the signal's sparsity against the residual error. The influences of the pilot distribution and the regularization parameter on the BPDN algorithm are discussed in the simulation. The BPDN channel estimator is compared with the least square (LS) and also with orthogonal matching pursuit (OMP). The data collected from lake experiment show that the BPDN channel estimator outperforms the LS and OMP channel estimator over spare underwater acoustic channel.