Comparison of Sparse Recovery Algorithms for Channel Estimation in Underwater Acoustic OFDM with Data-Driven Sparsity Learning

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Department of Electrical and Computer Engineering


Through exploiting the sparse nature of underwater acoustic (UWA) channels, compressed sensing (CS) based sparse channel estimation has demonstrated superior performance compared to the conventional least-squares (LS) method. However, a priori information of channel sparsity is often required to set a regularization constraint. In this work, we propose a data-driven sparsity learning approach based on a linear minimum mean square error (LMMSE) equalizer to tune the regularization parameter for the orthogonal frequency division multiplexing (OFDM) transmissions. A golden section search is used to accelerate the sparsity learning process. In the context of the intercarrier interference (ICI)-ignorant and ICI-aware UWA OFDM systems, the block error rates (BLERs) using different sparse recovery algorithms for channel estimation under the L0, L1/2, L1, and L2 constraints are compared. Simulation and experimental results show that the data-driven sparsity learning approach is effective, overcoming the drawback of using a fixed regularization parameter in different channel conditions. When the sparsity parameter for each approach is optimized based on the data-driven approach, the L1/2 recovery algorithm and the considered four L1 recovery algorithms: SpaRSA, FISTA, Nesterov, and TwIST, have nearly the same BLER performance, outperforming L0 and L2 algorithms.

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© 2014 Elsevier B.V.

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Physical Communication