Using selective partial update-selective regressor affine projection algorithms for adaptive equalization in underwater acoustic communications

Interference signals due to scattering from surface and reflecting from bottom is one of the most important problems of reliable communications in shallow water channels. For solving this problem, one of the best suggested ways is to use adaptive equalizers. Convergence rate and misadjustment error in adaptive algorithms have important roles in adaptive equalizer performance. In this paper, affine projection algorithm (APA), selective regressor (SR-APA) and selective partial update selective regressor affine projection algorithm (SPU-SR-APA) have been compared with conventional algorithms such as least mean square (LMS) in underwater acoustic communications. We apply experimental data from Persian Gulf for demonstrating the efficiency of the proposed methods over shallow water channel. We observe that the values of the steady-state mean square error (MSE) of SR-APA and SPU-SR-APA decrease in comparison with least mean square (LMS) algorithm. Also the family of SR-APA, SPU-SR-APA has better convergence rate than LMS type algorithm.