Distributed Online Learning for Time-Critical Communication in 6G Industrial Subnetworks

TL;DR

This paper introduces a distributed deep reinforcement learning protocol for medium access control in 6G industrial subnetworks, improving timely alarm delivery under challenging conditions.

Contribution

It presents a novel DRL-based decentralized protocol enabling local access points to autonomously optimize transmission timing for critical alarms.

Findings

Achieves higher probability of in-time alarm delivery than benchmark schemes.

Scales better with increasing network density, maintaining performance.

Improves alarm delivery probability by at least 7% at 40 subnetworks, up to 21% at 60 subnetworks.

Abstract

6G industrial in-X subnetworks are expected to support highly time-critical alarm reporting in large-scale environments characterized by mobility, bursty event-driven traffic, and limited radio resources. In such settings, conventional medium access solutions are ill-suited to guarantee reliable delivery of critical traffic, e.g., emergency alarms, within strict deadlines, especially when multiple subnetworks become simultaneously active after a common alarm event, a scenario widely referred as medium access with a shared message. This paper proposes a distributed deep reinforcement learning (DRL)-based medium access control protocol for timely alarm transmission in time-critical industrial subnetworks. The proposed method enables each local access point (LAP) to learn, in an online manner, to infer contention conditions from a broadcast contention-signature signal and to autonomously select a transmission pattern over the available channels using a lightweight deep neural network and an (ephsilon)-greedy policy. Simulation results demonstrate that the proposed approach consistently achieves a higher probability of in-time alarm delivery than benchmark random-access schemes, while exhibiting better scalability with increasing network density. For instance, the proposed method improves probability of in-time alarm delivery by at least 7% with a network size of 40 subnetworks, while the gain increases to 21% when the number of subnetworks increases to 60.