Switching LMS linear turbo equalization

Turbo equalization using linear filters for data detection has been shown to perform nearly as well as those based on the original maximum a posteriori probability (MAP) detection approach. Such linear equalization methods have taken on many forms in the literature, from simple least-mean-square (LMS)-based adaptive filtering approaches, to minimum mean square error (MMSE)-based methods that are recursively computed for each output symbol for each iteration. In this paper, we consider a class of turbo equalization algorithms in which complexity requirements dictate that a fixed set of filter coefficients must be used for all symbols and for all iterations. By computing one such set of coefficients via the LMS algorithm assuming unreliable soft information, and another set assuming highly reliable soft information, we show that a switching strategy can be employed, nearly achieving the performance of recomputing the coefficients at each iteration.