In this paper, we consider the problem of real-time transmission scheduling over time-varying channels. We first formulate the transmission scheduling problem as a Markov decision process (MDP) and systematically unravel the structural properties (e.g. concavity in the state-value function and monotonicity in the optimal scheduling policy) exhibited by the optimal solutions. We then propose an online learning algorithm which preserves these structural properties and achieves -optimal solutions for an arbitrarily small . The advantages of the proposed online method are that: (i) it does not require a priori knowledge of the traffic arrival and channel statistics and (ii) it adaptively approximates the state-value functions using piece-wise linear functions and has low storage and computation complexity. We also extend the proposed low-complexity online learning solution to the prioritized data transmission. The simulation results demonstrate that the proposed method achieves significantly better utility (or delay)-energy trade-offs when comparing to existing state-of-art online optimization methods.
Optimal scheduling of island integrated energy systems considering multi-uncertainties and hydrothermal simultaneous transmission: A deep reinforcement learning approach
Northeast Electric Power University（东北电力大学）；Huazhong University of Science and Technology（华中科技大学）；Cranfield University（克兰菲尔德大学）
Towards Secure and Efficient Data Scheduling for Vehicular Social Networks
Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)（广东省人工智能与数字经济实验室（深圳））；College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机与软件学院）；College of Electronics and Information Engineering, Tongji University（同济大学电子与信息工程学院）；School of Science, Computing and Engineering Technologies, Swinburne University of Technology（斯威本科技大学科学、计算与工程技术学院）
Improving Pattern Recognition of Scheduling Anomalies through Structure-Aware and Semantically-Enhanced Graphs
Carnegie Mellon University（卡内基梅隆大学）；Illinois Institute of Technology（伊利诺伊理工学院）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Duke University（杜克大学）；Northeastern University（东北大学）
Age Aware Scheduling for Differentially-Private Federated Learning
Institute of Communications Engineering, National Yang Ming Chiao Tung University（国立阳明交通大学通信工程研究所）；Simula UiB（西挪威应用科技大学西蒙研究所）；Department of Communication Engineering, National Taipei University（国立台北大学通信工程学系）
Green Federated Learning via Carbon-Aware Client and Time Slot Scheduling
Inria Centre at Université Côte d’Azur（法国国家信息与自动化技术研究院（Inria）亚维农分校）；Accenture（埃森哲）；Department of Electrical Engineering (ISY), Linköping University（利厄普大学电气工程系）
