GRLinQ: An Intelligent Spectrum Sharing Mechanism for Device-to-Device Communications with Graph Reinforcement Learning
<|reference_start|>GRLinQ: An Intelligent Spectrum Sharing Mechanism for Device-to-Device Communications with Graph Reinforcement Learning: 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.<|reference_end|>
@article{shan2024grlinq:,
    title={GRLinQ: An Intelligent Spectrum Sharing Mechanism for Device-to-Device
  Communications with Graph Reinforcement Learning},
    author={Zhiwei Shan, Xinping Yi, Le Liang, Chung-Shou Liao, and Shi Jin},
    journal={arXiv preprint arXiv:2408.09394},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.09394},
    primaryClass={cs.NI cs.IT cs.LG math.IT}
}