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.

Deep Learning for Secure Transmission in Reconfigurable Intelligent Surface-Assisted Communications

This paper investigates the secure transmission for reconfigurable intelligent surface(RIS)-assisted wireless communication systems.In the studied model,one user connects to the access point via a RIS while an eavesdropper eavesdrops on the signal sent from the user to the access point.Therefore,it is necessary to design an appropriate RIS reflection vector to solve the eavesdropping problem.This problem is formulated as an optimization problem whose goal is to maximize the secure energy efficiency which is defined as the ratio of the secure rate to the total energy consumption of the system via jointly optimizing the RIS reflection reflector as well as the number of RIS elements,which results in a non-convex optimization problem that is intractable to solve by traditional methods.To tackle this issue,a new algorithm by leveraging the advance of the established deep learning(DL)technique is proposed so as to find the optimal RIS reflection vector and determine the optimal number of RIS reflection elements.Simulation results show that the proposed method reaches 96%of the optimal secure energy efficiency of the genie-aided algorithm.