Underwater Acoustic OFDM Channel Estimation Based on Bayesian Compressive Sensing

Underwater acoustic channel (UWAC) has the feature of sparse multipath. In traditional channel estimation algorithms, however, this sparse multipath feature is not considered, in order to obtain higher estimation accuracy, it take lots of frequency band, resulting in a waste of communication resources. In this paper, a sparse channel estimation approach based Bayesian Compressive sensing (BCS) is proposed in OFDM UWA communication. Owing to the sparsity of the UWAC, only a few pilot signals are needed to recover the channel impulse response (CIR) with a high accuracy, which effectively improve the utilization of spectrum, energy and so on. The simulation process analyzes the influence of pilot number, signal-to-noise ratio on channel estimation performance of proposed algorithm, Orthogonal Matching Pursuit (OMP) and Least Square (LS). According to the simulation result, under the condition of sparse channel the channel, estimation method based on the algorithm proposed in this paper enjoys better performance than OMP and LS.