Antenna selection in energy harvesting relaying networks using Q-learning algorithm

In this paper, a novel opportunistic scheduling (OS) scheme with antenna selection (AS) for the energy harvesting (EH) cooperative communication system where the relay can harvest energy from the source transmission is proposed. In this considered scheme, we take into both traditional mathematical analysis and reinforcement learning (RL) scenarios with the power splitting (PS) factor constraint. For the case of traditional mathematical analysis of a fixed-PS factor, we derive an exact closed-form expressions for the ergodic capacity and outage probability in general signal-to-noise ratio (SNR) regime. Then, we combine the optimal PS factor with performance metrics to achieve the optimal transmission performance. Subsequently, based on the optimized PS factor, a RL technique called as Q-learning (QL) algorithm is proposed to derive the optimal antenna selection strategy. To highlight the performance advantage of the proposed QL with training the received SNR at the destination, we also examine the scenario of QL scheme with training channel between the relay and the destination. The results illustrate that, the optimized scheme is always superior to the fixed-PS factor scheme. In addition, a better system parameter setting with QL significantly outperforms the traditional mathematical analysis scheme.