Variable step size LMS equalization algorithm based on adaptivemixed-power parameter in underwater acoustic channels

An improved novel variable step size least mean square (VSS-XENLMS) adaptive filtering algorithm is proposed and it is applied to underwater acoustic equalization. A variable mixed-power parameter λk is introduced whose the time variation allows the algorithm to follow fast changes in the channel. The proposed algorithm overcomes the dependency on the selection of the mixing parameter λ, which has been by developed normanized least mean square (XENLMS) algorithm. The selecting about three factors α, β and μ and their influences to convergence ability are analysed. Computer simulations of the proposed algorithm about convergence ability are carried out respectively under two underwater acoustic channels, using two modulation signals. Simulation results demonstrate that the convergence speed of the proposed algorithm compared with that of XENLMS algorithm and the former variable step-size algorithms has been visibly increased, the convergence performance of the proposed algorithm is compared to that of recursive least square (RLS), but its computation complexity is far less RLS. Then, Mulan Lake experiment shows that the performance of the decision feedback equalization (DFE)-based the proposed algorithm (VSS-XENLMS-DFE) is better than that of the LMS-DFE algorithm in terms of bit error rate for an order of magnitude, which overcomes the effects of multipath and Doppler shift very well.