用图神经网络和强化学习动态调度，提升工业无线网络的实时通信可靠性。
GCN-Driven Reinforcement Learning for Probabilistic Real-Time Guarantees in Industrial URLLC
- 用GCN捕捉网络拓扑关系，DQN根据实时状态自适应调整链路优先级。
- 相比传统方法，平均信干噪比提升超175%，最高达197.4%。
- 适合需要低时延高可靠的工业物联网场景，部署开销小。
在大规模工业无线网络中，保障包级通信质量对超可靠低时延通信（URLLC）至关重要。本文通过引入图卷积网络（GCN）与深度Q网络（DQN）强化学习框架，改进了局部截止时间划分（LDP）算法，以提升多小区多信道网络中的干扰协调能力。与LDP采用静态优先级不同，本方法基于实时流量需求、网络拓扑、剩余传输机会和干扰模式动态学习链路优先级。GCN用于捕捉空间依赖关系，DQN则通过奖励驱动探索实现自适应调度决策。仿真结果表明，该GCN-DQN模型在三种网络配置下，平均信干噪比（SINR）相较LDP分别提升179.6%、197.4%和175.2%；相较于此前基于CNN的方法，分别提升31.5%、53.0%和84.7%。结果验证了该模型在满足复杂URLLC需求的同时，具备极低开销和卓越性能。
原文摘要 · Abstract (English)
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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