A Deep CNN-based Relay Selection in EH Full-Duplex IoT Networks with Short-Packet Communications

Abstract

In this paper, we propose an efficient deep convolutional neural network-based relay selection (CNS) scheme to evaluate and improve the end-to-end throughput in energy harvesting full-duplex Internet-of-Things (IoT) networks. In this system, multiple full-duplex relays harvest energy from a power beacon to assist data transmission from a source node to multiple users under short packet communications. We propose a best relay best user (bR-bU) selection scheme to improve the diversity packet transmission. We then develop a deep convolutional neural network framework for relay selection and throughput prediction with high accuracy and low execution time. Simulation results show that the proposed CNS scheme achieves almost exactly the throughput of bR-bU one, while it considerably reduces computational complexity, suggesting a real-time configuration for IoT systems under complex scenarios. Moreover, the designed CNN model achieves the root-mean-square-error (RMSE) of 8.4 x 10(-3) on the considered dataset, which exhibits the lowest RMSE as compared to the deep neural network and state-of-the-art machine learning approaches.