Radio Resource Management is a challenging topic in future 6G networks where novel applications create strong competition among the users for the available resources. In this work we consider the frequency scheduling problem in a multi-user MIMO system. Frequency resources need to be assigned to a set of users while allowing for concurrent transmissions in the same sub-band. Traditional methods are insufficient to cope with all the involved constraints and uncertainties, whereas reinforcement learning can directly learn near-optimal solutions for such complex environments. However, the scheduling problem has an enormous action space accounting for all the combinations of users and sub-bands, so out-of-the-box algorithms cannot be used directly. In this work, we propose a scheduler based on action-branching over sub-bands, which is a deep Q-learning architecture with parallel decision capabilities. The sub-bands learn correlated but local decision policies and altogether they optimize a global reward. To improve the scaling of the architecture with the number of sub-bands, we propose variations (Unibranch, Graph Neural Network-based) that reduce the number of parameters to learn. The parallel decision making of the proposed architecture allows to meet short inference time requirements in real systems. Furthermore, the deep Q-learning approach permits online fine-tuning after deployment to bridge the sim-to-real gap. The proposed architectures are evaluated against relevant baselines from the literature showing competitive performance and possibilities of online adaptation to evolving environments.
Bi-Level Online Provisioning and Scheduling with Switching Costs and Cross-Level Constraints
Dept. of Electrical Engineering University at Buffalo, Buffalo, NY（电气工程系 网络布法罗大学）；Computer Engineering The Ohio State University, Columbus, OH（计算机工程 俄亥俄州立大学）
Adapter-Augmented Bandits for Online Multi-Constrained Multi-Modal Inference Scheduling
School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China.（中山大学计算机科学与工程学院）；School of Computing, Macquarie University, NSW 2109, Australia（麦考瑞大学计算机学院）
AEGIS: Risk-Budgeted Online Scheduling for Resilient Continuous Edge Inference
Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University（同济大学上海智能自主系统研究院）；College of Big Data and Information Engineering, Guizhou University（贵州大学大数据与信息工程学院）；School of Engineering, Edith Cowan University（伊迪丝科文大学工程学院）
