Reliable and Energy-Efficient MAC Protocols in Industrial IoT Networks via Multi-Agent Reinforcement Learning

The transition from wired to wireless communications in industrial Internet of Things (IIoT) networks introduces stringent challenges in terms of reliability and energy efficiency, aggravated by harsh propagation conditions and contention for a shared radio medium. These constraints require advanced medium access control (MAC) protocols capable of jointly managing channel access, packet retransmissions, and buffer operations while accounting for the battery limitations of IoT devices (IoTDs). This paper proposes a multi-agent reinforcement learning (MARL) framework for the autonomous design of energy-efficient and reliable MAC protocols in uplink wireless IIoT networks supporting time-frequency multiplexing. Moving away from conventional decentralized partially observable Markov decision process (Dec-POMDP)-based MARL designs, the framework adopts a partially observable Markov game (POMG), thereby enabling per-device policy learning. A novel reward mechanism is introduced, in which the base station broadcasts a resource-level feedback, and each device constructs a local reward based solely on its own observations and past actions, ensuring feasibility in real deployments. Simulation results show that the proposed framework achieves maximum reliability, whereas state-of-the-art MARL benchmarks based on global rewards fail to meet the required target, highlighting the importance of POMG modeling and local reward structures for reliable wireless IIoT networks. Furthermore, a comparison with conventional grant-based protocols, which inherently achieve maximum reliability, demonstrates that the proposed solution significantly reduces the active-mode duration, thereby improving overall energy efficiency.