Adaptive Sparse Channel Estimation Based on RLS for Underwater Acoustic OFDM Systems

Channel estimation has been becoming the key technique for adaptive underwater acoustic communication systems in tracking fast-fading channels. In this paper, by exploiting the sparse features of underwater acoustic channels for orthogonal frequency division multiplexing (OFDM) systems, we focus on investigating an adaptive channel estimator based on least squared (LS) and recursive least-squares (RLS). The complexity of the proposed method is low in that only a small number of nonzero-value channel paths are refined by RLS algorithm. Different channel estimation methods are compared for proving the performance of the proposed method. Simulation results show that our proposed method has a better performance, and it is able to track underwater acoustic channels well and provide reliable channel estimates to the communication system.