Underwater Acoustic Channel Estimation for Pilot-based OFDM using Compressed Sensing

The objective of this research is to investigate state-of-the-art channel estimation techniques for underwater acoustic (UWA) communication that utilize compressed sensing (CS). The channel estimation problem for multiple-input multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) systems with pilot tones is formulated as a compressive sensing problem with sparse multipath channels. A novel algorithm called detection-based orthogonal matching pursuit (DOMP,) which extends the orthogonal matching pursuit (OMP) algorithm by incorporating a binary hypothesis test on the residual vector to estimate the sparsity level, is applied. The performance of DOMP is compared with OMP, compressive sampling matching pursuit (CoSaMP), and least square (LS) algorithms in terms of mean square error (MSE) and bit error rate (BER) under various signal-to-noise ratio (SNR) scenarios using both regular and random pilot arrangements. It has been shown by the results that compressive sensing-based methods are better than the conventional LS algorithm for MIMO-OFDM UWA communication systems.