LMS-BLM receiver in AF based cooperative relay networks

Abstract

In this paper, we assume that little is known about channel state information (CSI) at the receiver in order to recover the unknown transmitted signal. Therefore, channel estimation and data detection are the most useful techniques at the receiver in wireless communication networks for recovering the original transmitted signal. In our study, we propose an algorithm based on least mean square (LMS) algorithm and Bayesian linear model (BLM) detector for their robustness and low implementation complexity. Amplify and forward (AF) based cooperative relay networks is considered in this study for its capability to mitigate the effects caused by multipath fading and shadowing. Analytical and simulation results on receiver operating characteristics (ROC) and MSE learning curves are presented. The results show that the proposed algorithm is effective and can provide higher detection performance in the current and next generation wireless networks.