mlsanityData to 14 Oct 2026

GRLinQ: An Intelligent Spectrum Sharing Mechanism for Device-to-Device Communications with Graph Reinforcement Learning

arXiv 2408.09394 ↗First posted 18 Aug 2024cs.NI cs.IT cs.LGcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

Device-to-device (D2D) spectrum sharing in wireless communications is a challenging non-convex combinatorial optimization problem, involving entangled link scheduling and power control in a large-scale network. The state-of-the-art methods, either from a model-based or a data-driven perspective, exhibit certain limitations such as the critical need for channel state information (CSI) and/or a large number of (solved) instances (e.g., network layouts) as training samples. To advance this line of research, we propose a novel hybrid model/datadriven spectrum sharing mechanism with graph reinforcement learning for link scheduling (GRLinQ), injecting information theoretical insights into machine learning models, in such a way that link scheduling and power control can be solved in an intelligent yet explainable manner. Through an extensive set of experiments, GRLinQ demonstrates superior performance to the existing model-based and data-driven link scheduling and/or power control methods, with a relaxed requirement for CSI, a substantially reduced number of unsolved instances as training samples, a possible distributed deployment, reduced online/offline computational complexity, and more remarkably excellent scalability and generalizability over different network scenarios and system configurations.

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 14 of its 32 references that are in the map. First posted on arXiv 18 Aug 2024.

Builds on

14 of 32 references.

References · 14

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
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  7. 2018Model-Driven Deep Learning for Physical Layer Communicationscited by 51
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  9. 2020Iterative Algorithm Induced Deep-Unfolding Neural Networks: Precoding Design for Multiuser MIMO Systemscited by 44
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2 more
  1. 2019Deep Learning based Wireless Resource Allocation with Application to Vehicular Networkscited by 20
  2. 2023SCENE: Reasoning about Traffic Scenes using Heterogeneous Graph Neural Networkscited by 18

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  2. 2025Power Allocation for Delay Optimization in Device-to-Device Networks: A Graph Reinforcement Learning Approach
  3. 2026Graph Signal Diffusion Models for Wireless Resource Allocation
  4. 2024AdapShare: An RL-Based Dynamic Spectrum Sharing Solution for O-RAN
  5. 2025Fair Dynamic Spectrum Access via Fully Decentralized Multi-Agent Reinforcement Learning
  6. 2026Multi-Agent Spectrum Sharing
  7. 2025Meta-Reinforcement Learning for Fast and Data-Efficient Spectrum Allocation in Dynamic Wireless Networks
  8. 2025AI-Driven Green Cognitive Radio Networks for Sustainable 6G Communication