Underwater acoustic channel (UAC) is one of the most challenging communication channels in the world, owing to\nits complex multi-path and absorption as well as variable ambient noise. Although adaptive equalization could\neffectively eliminate the inter-symbol interference (ISI) with the help of training sequences, the convergence rate of\nequalization in sparse UAC decreased remarkably. Besides, channel estimation algorithms could roughly figure out\nchannel impulse response and other channel parameters through several specific mathematical criterions. In this\npaper, a typical channel estimation method, least square (LS) algorithm, is applied in adaptive equalization to obtain\nthe initial tap weights of least mean square (LMS) algorithm. Simulation results show that the proposed method\nsignificantly enhances the convergence rate of the LMS algorithm.
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