Reinforcement Learning-Empowered Mobile Edge Computing for 6G Edge Intelligence
Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）；Tsinghua University（清华大学）；East China Normal University（华东师范大学）；Nantong University（南通大学）；National Mobile Communications Research Laboratory（移动通信国家重点实验室）；Southeast University（东南大学）；National Key Laboratory on Communications（通信抗干扰技术国家级重点实验室）；University of Electronic Science and Technology of China（电子科技大学）
Peng Wei, Kun Guo, Ye Li, Jue Wang, Wei Feng, Shi Jin, Ning Ge, Ying-Chang Liang
Mobile edge computing (MEC) is considered a novel paradigm for computation-intensive and delay-sensitive tasks in fifth generation (5G) networks and beyond. However, its uncertainty, referred to as dynamic and randomness, from the mobile device, wireless channel, and edge network sides, results in high-dimensional, nonconvex, nonlinear, and NP-hard optimization problems. Thanks to the evolved reinforcement learning (RL), upon iteratively interacting with the dynamic and random environment, its trained agent can intelligently obtain the optimal policy in MEC. Furthermore, its evolved versions, such as deep RL (DRL), can achieve higher convergence speed efficiency and learning accuracy based on the parametric approximation for the large-scale state-action space. This paper provides a comprehensive research review on RL-enabled MEC and offers insight for development in this area. More importantly, associated with free mobility, dynamic channels, and distributed services, the MEC challenges that can be solved by different kinds of RL algorithms are identified, followed by how they can be solved by RL solutions in diverse mobile applications. Finally, the open challenges are discussed to provide helpful guidance for future research in RL training and learning MEC.
Artificial Intelligence Empowered Multiple Access for Ultra Reliable and Low Latency THz Wireless Networks
University of Piraeus（比雷埃夫斯大学）；RapidMiner GmbH（RapidMiner有限公司）；Nokia Standardization Department（诺基亚标准化部）；Université Paris-Saclay（巴黎萨克雷大学）；CNRS（法国国家科学研究中心）；CentraleSupélec（巴黎中央理工-高等电力学院）
BRAIN: Bayesian Reasoning via Active Inference for Agentic and Embodied Intelligence in Mobile Networks
School of Electrical Engineering and Computer Science, TU Berlin（技术大学柏林电气工程与计算机科学学院）；Optical Zeitgeist Laboratory, INRS（光感知实验室，INRS）；Federal Ministry of Research, Technology and Space (BMFTR, Germany)（德国联邦研究、科技与航天部）
Converging Paradigms: The Synergy of Symbolic and Connectionist AI in LLM-Empowered Autonomous Agents
Microsoft Corporation（微软公司）；University of Virginia（弗吉尼亚大学）；Hong Kong University of Science and Technology (GZ)（香港科技大学（广州））；Nottingham Trent University（诺丁汉特伦特大学）；Silesian University of Technology（西里西亚工业大学）；Zayed University（扎耶德大学）
Joint Task Offloading, Inference Optimization and UAV Trajectory Planning for Generative AI Empowered Intelligent Transportation Digital Twin
Guangxi University Key Laboratory of Intelligent Networking and Scenario System (School of Information and Communication, Guilin University of Electronic Technology)（广西大学智能组网与场景系统重点实验室（桂林电子科技大学信息与通信学院））；Research Institute of Highway Ministry of Transport（交通运输部公路科学研究院）；School of Automation, Guangdong University of Technology（广东工业大学自动化学院）；School of Architecture and Transportation Engineering, Guilin University of Electronic Technology（桂林电子科技大学建筑与交通工程学院）
Agentic AI-Empowered Wireless Agent Networks With Semantic-Aware Collaboration via ILAC
Zhejiang University（浙江大学）；College of Information Science and Electronic Engineering, Zhejiang University（浙江大学信息与电子工程学院）；Zhejiang Provincial Key Laboratory of Info. Proc., Commun. & Netw. (IPCAN)（浙江省信息处理、通信与网络重点实验室）；King’s College London（伦敦国王学院）；Department of Engineering, King’s College London（伦敦国王学院工程系）；University of Miami（迈阿密大学）；Institute for Data Science and Computing, University of Miami（迈阿密大学数据科学与计算研究所）；The University of Hong Kong（香港大学）；Department of Electrical and Electronic Engineering, The University of Hong Kong（香港大学电子电气工程系）
