GRLinQ: A Distributed Link Scheduling Mechanism with Graph Reinforcement Learning

Device-to-Device (D2D) link scheduling in wireless communications is a challenging non-convex combinatorial optimization problem. The state-of-the-art methods, either from a model-based or a data-driven perspective, exhibit certain limitations such as the critical need of Channel State Information (CSI) and a large number of instances or solved instances as training samples. To advance this line of research, we propose a novel hybrid model/data-driven approach with Graph Reinforcement Learning for Link Scheduling (GRLinQ), injecting information theoretical insights into machine leaning models. GRLinQ demonstrates superior performance to the existing model-based and data-driven link scheduling mechanisms, with a relaxed requirement of CSI, a smaller number of unsolved instances as training samples, a possible distributed deployment, and more remarkably an excellent generalization ability over different network scenarios and system configurations.