Sparse Channel Estimation and Data Detection Algorithms for Ofdm-Based Underwater Acoustic Communication Systems

Abstract

Communication over acoustic signals in underwater results into a multi-scale multilag channels which occurs due to the multipath propagation. Hence a robust channel estimation technique has to be present at the receiver and the solutions of the terrestrial-based systems are not applicable. in this work using path-based channel model that characterizes underwater channels by a delay a Doppler shift and an attenuation factors three new pilot assisted time domain-based channel estimation algorithms are proposed for single-input single-output communicationbased and diversity communication-based underwater acoustic systems. The multicarrier transmission technique assumed is OFDM. in addition depending on the base stations deployment in underwater the sparse underwater channels undergo Rician or Rayleigh fading where channels in this work are generated using Bellhop software. in the first two proposed approach the overall sparse channel tap delays and constant Doppler shifts are estimated using Matching Pursuit and Orthogonal Matching Pursuit algorithms where the sparse complex channel path gain vector is estimated by maximum a posteriori probability (MAP) technique and the prior densities of the channel gains follow Rician distribution with unknown mean and variance vectors where Maximum Likelihood is proposed for their estimation. The first approach considers a colored noise and uniform Doppler spread and the second approach considers a non-uniform Doppler shifts with white noise. The third proposed approach considers transmitter diversity with Alamouti’s coding where the channel estimator iteratively estimates the complex channel parameters of each subcarrier using the expectation maximization method which in turn converges to a true maximum a posteriori probability estimation of the unknown channel where Karhunen-Loeve expansion and ESPRiT algorithm are assumed for complexity reduction and delay estimation respectively. Finally in ii order to assess the performance of the proposed algorithms the computer simulations show the behavior in terms of mean square error and symbol error rate.