A combined recursive least square and least mean square equalization scheme based on windowed error autocorrelation estimation

Abstract

Equalizer is widely applied in communication systems to eliminate Inter-Symbol Interference mainly caused by multipath over wireless channels. Various algorithms are developed for coefficients update of the equalizer when tracking the channel. However, advantages and drawbacks coexist for single updating algorithm. In this paper, instead of single algorithm applied in the whole frame, two algorithms, recursive least square (RLS) and least mean square (LMS), are intelligently combined in our algorithm. For each iteration, one of two algorithms is chosen by comparing the windowed estimated error autocorrelation with a pre-selected threshold. Since the combined algorithm reaches to convergence using RLS algorithm, the convergence rate is fast and the length of training sequence can be decreased as a result of the effective rate increase. Extended simulations show that our proposed combination algorithm has better mean square error (MSE) and bit error rate (BER) performance compared with single LMS algorithm and lower complexity compared with RLS algorithm. Moreover, the proposed algorithm can track time-varying channel with small performance degradation and dramatic complexity reduction.