Neural Network-Based Relay Selection in Two-Way SWIPT-Enabled Cognitive Radio Networks

Abstract

In this paper, we investigate the relay selection problem for two-way simultaneous wireless information and power transfer (SWIPT) enabled cognitive radio networks. The system we consider includes a pair of primary users and multiple secondary user transceivers, where the secondary transmitters are energy-constrained and employ the decode-and-forward scheme to assist the transmission between the primary users. Specifically, in order to improve the spectrum efficiency of the considered system, we design a novel two-way communication protocol for SWIPT-enabled cognitive radio networks. Furthermore, different from the traditional relay selection scheme, under this protocol, we propose two data-driven relay selection methods based on the neural network for the fixed number of relays and the variable number of relays, respectively. The simulation results demonstrate that the proposed methods can achieve better performance than the traditional relay selection as well as the traditional machine learning methods, and approach to the theoretical optimal value in most cases.