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

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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… Click to show full abstract

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.

Keywords: reinforcement learning; based reinforcement; transmission; latency; model based

Journal Title: IEEE Wireless Communications Letters
Year Published: 2023

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