Performance Analysis and Deep-Learning-Based Real-Time Evaluation for Multihop MIMO Full-Duplex Relay Networks with Short-Packet URLLCs

This work investigates multihop multiple-input multiple-output full-duplex relay (FDR) networks using short-packet communications in accordance with ultra-reliability and low-latency communications. To comprehensively capture the performance trend of the considered systems, the end-to-end block-error rate of the considered FDR is analyzed and compared with that of half-duplex relaying systems, from which the effective throughput (ETP), energy efficiency (EE), reliability, and latency are also studied. However, the derived analytical expressions contain non-elementary functions, making them intricate for practical implementations, particularly in real-time configurations. Motivated by this, we introduce a deep multiple-output neural network with a short execution time, low computational complexity, and highly accurate estimation. This network can serve as an efficient tool to rapidly respond to the necessary system parameters, such as transmit power and blocklength, when the services request specific ETP, EE, reliability, and latency. To validate the correctness of the theoretical analysis, extensive simulation results are provided under varying impacts of system parameters.