Short-packet communications in wireless energy transfer full-duplex IoT networks with deep learning design

In this paper, we study wireless energy transfer full-duplex (FD) Internet-of-things (IoT) networks, where multiple FD IoT relays are deployed to assist short-packet communications between a source and a robot destination with multiple antennas in automation factories. Considering two residual interference (RSI) models for FD relays, we propose a full relay selection (FRS) scheme to maximize the e2e signal-to-noise ratio of packet transmissions. We derive the closed-form expressions for the average block error rate (BLER) and throughput of the considered system, based on which the approximation analysis is also carried out. Towards real-time configurations, we design a deep learning framework based on the FRS scheme to accurately predict the average BLER and system throughput via a short inference process. Simulation results reveal the significant effects of RSI models on the performance of FD IoT networks. Furthermore, the CNN design achieves the lowest root-mean-squared error among other schemes such as the conventional CNN and deep neural network. Furthermore, the DL framework can estimate similar BLER and throughput values as the FRS scheme, but with significantly reduced complexity and execution time, showing the potential of DL design in dealing with complex scenarios of heterogeneous IoT networks.