Decimated least mean squares for frequency sparse channel estimation

The standard least mean squares (LMS) parameter estimation method does not assume any special structure for the parameters being estimated. However, when additional knowledge about the system is available, the performance of LMS can be improved by appropriate modification of the algorithm. We develop such modifications for the case of estimating frequency sparse channels. Such modifications provide either better performance or less complexity when compared to the standard LMS algorithm. Decimated LMS and zero attracting decimated LMS are the two methods proposed in this paper. Simulation results are also provided to compare the performance of the proposed algorithms to the standard LMS and other sparsity aware modifications of LMS.