Multihop MIMO Full-Duplex Relay Networks With Short-Packet URLLCs

This study explores multihop full-duplex relay (FDR) networks with multiple-input multiple-output capabilities, aiming to enhance short-packet ultra-reliability and low-latency communications. We derive closed-form expressions for performance metrics in terms of block-error rate, throughput, energy efficiency, reliability, and latency, from which an asymptotic analysis in the high signal-to-noise ratio regime is provided. Extensive simulations validate our theoretical analysis under varying system parameters. The findings indicate that the FDR performance is comparable to half-duplex relaying in specific scenarios. However, analytical expressions involve nonelementary functions, posing challenges for real-time configurations. To overcome this hurdle, we adopt machine-learning (ML) models for multioutput performance prediction with short execution time, low computational complexity, and high accuracy. Among the proposed ML frameworks, the extreme gradient boosting model with multi-output regressors proves to be the most efficient estimator. This network can rapidly respond with the necessary system settings to meet the desired quality of services associated with specific key performance indicators.