Distributed compressed sensing estimation of underwater acoustic OFDM channel

Abstract

Orthogonal frequency division multiplexing (OFDM) is recently drawing more and more attention for its high bandwidth efficiency over underwater acoustic (UWA) channels. However, the classic OFDM channel estimation algorithms, e.g. Least Square (LS), Minimum Mean Square Error (MMSE) are subject to significant performance degradation caused by doubly selective UWA channels. It has been recognized that the sparsity contained in UWA channels offers the possibility to improve the performance by compressed sensing (CS) estimation methods such as Orthogonal Matching Pursuit (OMP). Moreover, it has also been observed that multipath arrivals associated with adjacent OFDM symbols usually exhibit varying magnitude but similar delay, which means that UWA channels of several continuous symbols can be modeled as sparse sets with common support. In this paper, a Distributed Compressed Sensing (DCS) method is proposed to transform the problem of OFDM channel estimation into reconstruction of joint sparse signals. By exploiting this type of joint sparsity among adjacent OFDM symbols, we establish the DCS OFDM channel model, and then utilize the Simultaneous Orthogonal Matching Pursuit algorithm (SOMP) to optimize the model. Finally the experimental performance under field test is provided to illustrate the superiority of the proposed DCS channel estimation method, compared to the classic algorithm as well as CS counterparts.