Multiple Configured-Grants Optimization in Grant-Free NOMA for mURLLC Service

Abstract

Realizing efficient, delay-bounded, and reliable communications for a massive number of user equipments (UEs) in massive Ultra-Reliable and Low-Latency Communications (mURLLC) is extremely challenging as it needs to simultaneously take into account the latency, reliability, and massive access requirements. To support these requirements, the third generation partnership project (3GPP) has introduced grant-free non-orthogonal multiple access (GF-NOMA) with multiple configured-grants (MCGs), where UE can choose any of these grants as soon as the data arrives. In this paper, we develop a novel learning framework for MCG-GF-NOMA systems. We first design the MCG-GF-NOMA model by characterizing each CG. We then formulate the MCG-GF-NOMA resources configuration problem taking into account three constraints. Finally, we propose a Cooperative Multi-Agent based Double Deep Q-Network (CMA-DDQN) algorithm to allocate the channel resources among MCGs to maximize the number of successful transmissions under the latency constraint. Our results show that the MCG-GF-NOMA framework can simultaneously improve the low latency and high reliability performances for mURLLC.