  Massive Ultra-Reliable and Low-Latency Communications (mURLLC), which
integrates URLLC with massive access, is emerging as a new and important
service class in the next generation (6G) for time-sensitive traffics and has
recently received tremendous research attention. However, realizing efficient,
delay-bounded, and reliable communications for a massive number of user
equipments (UEs) in 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 enhanced grant-free (GF) transmission in the
uplink (UL), with multiple active configured-grants (CGs) for URLLC UEs. With
multiple CGs (MCG) for UL, UE can choose any of these grants as soon as the
data arrives. In addition, non-orthogonal multiple access (NOMA) has been
proposed to synergize with GF transmission to mitigate the serious transmission
delay and network congestion problems. In this paper, we develop a novel
learning framework for MCG-GF-NOMA systems with bursty traffic. We first design
the MCG-GF-NOMA model by characterizing each CG using the parameters: the
number of contention-transmission units (CTUs), the starting slot of each CG
within a subframe, and the number of repetitions of each CG. Based on the
model, the latency and reliability performances are characterized. 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 so as 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 in massive URLLC.
