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

arXiv: https://arxiv.org/abs/2506.15011 · HTML: https://mlsanity.com/paper/2506.15011/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 6 of its 15 references that are in the map.
- First posted on arXiv 17 Jun 2025.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 6 of 6)

- [A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems](https://mlsanity.com/paper/1902.10265/) (2019)
- [Asynchronous Hybrid Reinforcement Learning for Latency and Reliability Optimization in the Metaverse over Wireless Communications](https://mlsanity.com/paper/2212.14749/) (2022)
- [Scheduling with Probabilistic Per-Packet Real-Time Guarantee for URLLC](https://mlsanity.com/paper/2101.01768/) (2021)
- [Task-Oriented Delay-Aware Multi-Tier Computing in Cell-free Massive MIMO Systems](https://mlsanity.com/paper/2304.07079/) (2023)
- [Joint Uplink and Downlink Resource Allocation Towards Energy-efficient Transmission for URLLC](https://mlsanity.com/paper/2305.16105/) (2023)
- [Minimum Age TDMA Scheduling](https://mlsanity.com/paper/1905.10809/) (2019)

## Abstract (arXiv)

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.