A novel LMS-BLM algorithm for AF relays based cooperative wireless networks

Wireless communication networks are usually affected by channel impairments such as multi-path fading effects, shadowing, noise, etc. Therefore, techniques to mitigate wireless channel impairments, to improve the quality and spectral efficiency of communication over wireless channels are indispensable. Thus, amplify and forward (AF) relays based cooperative wireless networks are considered in this study for their capability to reduce the effects of fading caused by multipath propagation. Moreover, most studies on relay networks assume perfect channel state information at the receiver (CSIR), but in reality, due to the nature of wireless channels, little information is known on both the channel state information (CSI) and original transmitted signal at the receiver. Therefore, our main contribution is based on proposing a novel algorithm that combines the least mean square (LMS) algorithm and Bayesian linear model (BLM) detector for their low implementation complexity and feasibility. Analytical and theoretical simulations on receiver operating characteristics (ROC), complementary ROC, mean square errors (MSE) learning curves, optimum threshold and probability of error are presented. The results have shown that the proposed algorithm is effective and can provide higher detection performance in the current and next generation wireless networks where multiple relays strategy is applied.