Performance Comparison of LMS and RLS in Adaptive Equalization for Different Channel Conditions

The Least Mean Squares (LMS) algorithm is an important member of the family of stochastic gradient algorithms. A significant feature of the LMS algorithm is its simplicity. The recursive least squares (RLS) algorithm recursively finds the filter coefficients for minimizing linear least squares cost function. Adaptive equalization is capable of tracking a slowly time-varying channel response. For given channel models the LMS and RLS algorithms’ performance is simulated by using MATLAB and it is clearly obtained that the RLS algorithm is better than LMS algorithm because it provides faster and better convergence. It seems more stable than LMS algorithm.