Blind adaptive MMSE equalization of underwater acoustic channels based on the linear prediction method

Abstract

The problem of blind adaptive equalization of underwater single-input multiple-output (SIMO) acoustic channels was analyzed by using the linear prediction method. Minimum mean square error (MMSE) blind equalizers with arbitrary delay were described on a basis of channel identification. Two methods for calculating linear MMSE equalizers were proposed. One was based on full channel identification and realized using RLS adaptive algorithms, and the other was based on the zero-delay MMSE equalizer and realized using LMS and RLS adaptive algorithms, respectively. Performance of the three proposed algorithms and comparison with two existing zero-forcing (ZF) equalization algorithms were investigated by simulations utilizing two underwater acoustic channels. The results show that the proposed algorithms are robust enough to channel order mismatch. They have almost the same performance as the corresponding ZF algorithms under a high signal-to-noise (SNR) ratio and better performance under a low SNR.

Wavelet Frequency Domain Weighted Multi-modulus Blind Equalization Algorithm Based on Lower Order Statistics

“…In the Gaussian noise environment, we usually use two-order statistics of signals as an important means of analyzing and processing signals, such as the minimum mean square error criterion [7]. In a-stable distribution noise, we use minimum dispersion coefficient criterion, which is similar to the mean square error criterion, to analyze and process signals, and realize the minimization of average amplitude of estimation error.…”

Adaptive and efficient nonlinear channel equalization for underwater acoustic communication

“…However, due to poor physical link quality, high latency, constant movement of waves and chemical properties of water, the underwater acoustic channel is considered as one of the most adverse communication mediums in use today [3][4][5]. These adverse properties of the underwater acoustic channel should be equalized by in order to provide reliable communication [2,3,[6][7][8][9][10][11][12][13]. Furthermore, due to rapidly changing and unpredictable nature of underwater environment, constant movement of waves and transmitter-receivers, such processing should be adaptive [2,7,9,11].…”

“…Although linear equalization is the simplest equalization method, it delivers an extremely inferior performance compared to that of the optimal methods, such as Maximum A Posteriori (MAP) or Maximum Likelihood (ML) methods [10,17,18]. Nonetheless, the high complexities of the optimal methods, and also their need of the channel information [8,10,12,19,20], make them practically infeasible for UWA channel equalization, because of the extremely large delay spread of UWA channels [8,18,[21][22][23]. Hence, we seek to provide powerful nonlinear equalizers with low complexities as well as linear ones.…”