Ultra-reliable low-latency communication (URLLC) enables diverse applications with rigorous latency and reliability requirements. To provide a wide range of services, the future beyond fifth (B5G) systems are expected to support a large number of URLLC users. In this paper, we propose a joint sub-channel allocation and power control method to support massive access for non-orthogonal multiple access aided URLLC (NOMA-URLLC) networks. We model the problem of maximizing the number of successful access users as a multi-agent reinforcement learning problem. A deep Q-network-based multi-agent reinforcement learning (DQN-MARL) algorithm is proposed to tackle the problem while guaranteeing reliability and latency requirements of URLLC services. Simulation results show that the proposed DQN-MARL algorithm significantly improves the successful access probability in massive access scenarios compared with the existing schemes.
- Electronic mail,
- Massive access,
- multi-agent reinforcement learning,
- NOMA,
- NOMA,
- Power control,
- Reinforcement learning,
- Resource management,
- Ultra reliable low latency communication,
- Uplink,
- URLLC
DOI Link: https://doi.org/10.1109/TVT.2023.3292423
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