A Distributed Neural Linear Thompson Sampling Framework to Achieve URLLC in Industrial IoT
This work addresses the latency-reliability trade-off in IIoT networks, which is critical for supporting production chains, but it appears incremental as it adapts an existing algorithm to a specific domain.
The paper tackles the problem of optimizing uplink resource scheduling in Industrial IoT networks to achieve Ultra-Reliable Low-Latency Communication (URLLC) by proposing DISNETS, a distributed multi-agent reinforcement learning framework that reduces collisions without additional message exchange, demonstrating superior performance compared to baselines.
Industrial Internet of Things (IIoT) networks will provide Ultra-Reliable Low-Latency Communication (URLLC) to support critical processes underlying the production chains. However, standard protocols for allocating wireless resources may not optimize the latency-reliability trade-off, especially for uplink communication. For example, centralized grant-based scheduling can ensure almost zero collisions, but introduces delays in the way resources are requested by the User Equipments (UEs) and granted by the gNB. In turn, distributed scheduling (e.g., based on random access), in which UEs autonomously choose the resources for transmission, may lead to potentially many collisions especially when the traffic increases. In this work we propose DIStributed combinatorial NEural linear Thompson Sampling (DISNETS), a novel scheduling framework that combines the best of the two worlds. By leveraging a feedback signal from the gNB and reinforcement learning, the UEs are trained to autonomously optimize their uplink transmissions by selecting the available resources to minimize the number of collisions, without additional message exchange to/from the gNB. DISNETS is a distributed, multi-agent adaptation of the Neural Linear Thompson Sampling (NLTS) algorithm, which has been further extended to admit multiple parallel actions. We demonstrate the superior performance of DISNETS in addressing URLLC in IIoT scenarios compared to other baselines.