A Distributed Neural Linear Thompson Sampling Framework to Achieve URLLC
  in Industrial IoT
Francesco Pase Marco Giordani Sara Cavallero Malte Schellmann Josef Eichinger Roberto Verdone Michele Zorzi
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.