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

• URL: http://arxiv.org/abs/2408.09394v1
• Date: Sun, 18 Aug 2024 07:39:01 GMT
• Title: GRLinQ: An Intelligent Spectrum Sharing Mechanism for Device-to-Device Communications with Graph Reinforcement Learning
• Authors: Zhiwei Shan, Xinping Yi, Le Liang, Chung-Shou Liao, Shi Jin,
• Abstract summary: Device-to-device (D2D) spectrum sharing in communications is a challenging non- wireless optimization problem. We propose a novel model/datadriven spectrum sharing mechanism with graph reinforcement learning for link (GRLinQ) GRLinQ demonstrates superior performance to the existing model-based link scheduling and/or power control methods.
• Score: 36.37521131173745
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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