GCN-Driven Reinforcement Learning for Probabilistic Real-Time Guarantees in Industrial URLLC

TL;DR

This paper introduces a GCN-enhanced reinforcement learning approach to improve interference management and ensure ultra-reliable, low-latency communication in large-scale industrial wireless networks, outperforming existing static methods.

Contribution

It presents a novel GCN-DQN framework that dynamically learns link priorities considering network topology and traffic, significantly enhancing interference coordination in URLLC scenarios.

Findings

Achieves up to 197.4% SINR improvement over LDP.

Outperforms previous CNN-based approach by up to 84.7% SINR.

Demonstrates effective real-time adaptive scheduling with minimal overhead.

Abstract

Ensuring packet-level communication quality is vital for ultra-reliable, low-latency communications (URLLC) in large-scale industrial wireless networks. We enhance the Local Deadline Partition (LDP) algorithm by introducing a Graph Convolutional Network (GCN) integrated with a Deep Q-Network (DQN) reinforcement learning framework for improved interference coordination in multi-cell, multi-channel networks. Unlike LDP's static priorities, our approach dynamically learns link priorities based on real-time traffic demand, network topology, remaining transmission opportunities, and interference patterns. The GCN captures spatial dependencies, while the DQN enables adaptive scheduling decisions through reward-guided exploration. Simulation results show that our GCN-DQN model achieves mean SINR improvements of 179.6\%, 197.4\%, and 175.2\% over LDP across three network configurations. Additionally, the GCN-DQN model demonstrates mean SINR improvements of 31.5\%, 53.0\%, and 84.7\% over our previous CNN-based approach across the same configurations. These results underscore the effectiveness of our GCN-DQN model in addressing complex URLLC requirements with minimal overhead and superior network performance.