Channel tracking with WLMS algorithms: high performance at LMS computational load

Adaptation algorithms with constant gains are designed for tracking time-varying parameters of linear regression models with stationary regressors, in particular channel models in mobile radio communications. In a companion paper, an application to channel tracking in the IS-136 TDMA system is discussed. We propose algorithms that are based on two key concepts. First, the design is transformed into a Wiener filtering problem. Second, the parameters are modeled as correlated ARIMA processes with known dynamics. This leads to a new framework for systematic and optimal design of simple adaptation laws based on a priori information. The simplest adaptation law, named the Wiener LMS algorithm, is presented. In the design, all parameters are assumed to be governed by the same dynamics and the covariance matrix of the regressors is assumed to be known. The computational complexity is of the same order of magnitude as that of LMS for white regressors. The tracking performance is however substantially improved