ML-based selection relay with transmission power constraint

An effective relay selection scheme is developed using likelihood decision rule for two-hop stationary multi-relaying network. This scheme is based on the concept that we select the relay node most likely to have the highest probability of channel power gain distribution across the cluster of relaying links. By utilizing an amplify-and-forward (AF) relaying scenario, our selection criterion is subject to the channel power gains of the first relay hop, rather than the comparative instantaneous end-to-end signal-to-noise power ration (SNR) across relay hops, like full channel state information (CSI)-assisted relaying. Hence, the computational load for the relay selection process is substantially reduced. Analytical and simulation results demonstrate that, in terms of outage probability and energy efficiency, our proposed relay selection scheme significantly outperforms the conventional scheme which uses instantaneous SNR across relay hops, and is particularly suitable for a two-hop stationary AF relaying network.