Performance of the LMS algorithm on the estimation of time-varying channels

The performance of the least-mean-square (LMS) algorithm on the estimation of time-varying channels is analytically evaluated, using the estimation error correlation matrix and the mean square error (MSE) as parameters. Expressions for those parameters are obtained from a set of hypotheses usually adopted in the communication systems context. The channel is modeled as a wide sense stationary discrete time stochastic field with known autocorrelation. The expression for the steady state MSE is particularized assuming wide sense stationary-uncorrelated scattering (WSS-US) channel model. Several estimates of MSE curves obtained by computer simulation are compared with analytical results for validation purposes. A very good agreement between simulated and analytical results is shown.