Linear and non-linear space diversity combining algorithms over fading channels in TDMA systems

This paper investigates the performances of various adaptive algorithms for space diversity combining in time division multiple access (TDMA) digital cellular mobile radio systems. Two linear adaptive algorithms are investigated, the least mean square (LMS) and the square root Kalman (SRK) algorithm. These algorithms are based on the minimization of the mean-square error. However, the optimal performance can only be obtained using algorithms satisfying the minimum bit error rate (BER) criterion. This criterion can be satisfied using non-linear signal processing techniques such as artificial neural networks. An artificial neural network combiner model is developed, based on the recurrent neural network (RNN) structure, trained using the real-time recurrent learning (RTRL) algorithm. It is shown that, for channels characterized by Rician fading, the artificial neural network combiners based on the RNN structure are able to provide significant improvements in the BER performance in comparison with the linear techniques. In particular, improvements are evident in time-varying channels dominated by inter-symbol interference. Copyright (C) 2001 John Wiley & Sons, Ltd.