Novel Adaptive Transmission Scheme for Effective URLLC Support in 5G NR: A Model-Based Reinforcement Learning Solution

Future industrial Internet of Things (IIoT) applications demand trustworthy ultra-reliable and low-latency communications (URLLC) service. In this letter, we jointly design available reliability and latency mechanisms in 5G NR to maximize the probability of successful data delivery subject to a strict latency constraint. Particularly, we propose to optimally select numerology, mini-slot size, and modulation and coding scheme for each transmission/retransmission attempt, considering channel quality and remaining latency budget. To obtain the optimal policy for this sequential decision-making problem, we apply model-based reinforcement learning technique and formulate and solve a finite-step MDP problem. Through selected numerical examples, we show that the proposed joint design can achieve considerable performance gain over conventional scheme.