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

• URL: http://arxiv.org/abs/2506.15011v2
• Date: Mon, 07 Jul 2025 20:38:38 GMT
• Title: GCN-Driven Reinforcement Learning for Probabilistic Real-Time Guarantees in Industrial URLLC
• Authors: Eman Alqudah, Ashfaq Khokhar,
• Abstract summary: 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.<n>We show that our GCN-DQN model achieves mean SINR improvements of 179.6%, 197.4%, and 175.2% over LDP across three network configurations.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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