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

Abstract

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.