Edge Intelligence for Energy-efficient Computation Offloading and Resource Allocation in 5G Beyond
University of Electronic Science and Technology of China（电子科技大学）；University of Oslo（奥斯陆大学）；Simula Metropolitan Center for Digital Engineering（Simula大都会数字工程中心）
5G beyond is an end-edge-cloud orchestrated network that can exploit heterogeneous capabilities of the end devices, edge servers, and the cloud and thus has the potential to enable computation-intensive and delay-sensitive applications via computation offloading. However, in multi user wireless networks, diverse application requirements and the possibility of various radio access modes for communication among devices make it challenging to design an optimal computation offloading scheme. In addition, having access to complete network information that includes variables such as wireless channel state, and available bandwidth and computation resources, is a major issue. Deep Reinforcement Learning (DRL) is an emerging technique to address such an issue with limited and less accurate network information. In this paper, we utilize DRL to design an optimal computation offloading and resource allocation strategy for minimizing system energy consumption. We first present a multi-user end-edge-cloud orchestrated network where all devices and base stations have computation capabilities. Then, we formulate the joint computation offloading and resource allocation problem as a Markov Decision Process (MDP) and propose a new DRL algorithm to minimize system energy consumption. Numerical results based on a real-world dataset demonstrate that the proposed DRL-based algorithm significantly outperforms the benchmark policies in terms of system energy consumption. Extensive simulations show that learning rate, discount factor, and number of devices have considerable influence on the performance of the proposed algorithm.
Revenue and Energy Efficiency-Driven Delay Constrained Computing Task Offloading and Resource Allocation in a Vehicular Edge Computing Network: A Deep Reinforcement Learning Approach
AoI-Aware Task Offloading and Transmission Optimization for Industrial IoT Networks: A Branching Deep Reinforcement Learning Approach
Laboratory of Future Networks, University of Science and Technology of China（未来网络实验室，中国科学技术大学）；Deep Space Exploration Laboratory（深空探测实验室）；Department of Computing, The Hong Kong Polytechnic University（computing系，香港理工大学）
Joint Task Offloading and Resource Allocation in Low-Altitude MEC via Graph Attention Diffusion
School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机学院）；School of Cyber Engineering, Xidian University（西安电子科技大学电子工程学院）；Center for 6G Technology, Khalifa University of Science and Technology（卡塔尔科学技术大学6G技术中心）；School of Electrical and Electronics Engineering, Nanyang Technological University（南洋理工大学电子与电气工程学院）
A Multi-Head Ensemble Multi-Task Learning Approach for Dynamical Computation Offloading
Northwestern Polytechnical University（西北工业大学）；Xidian University（西安电子科技大学）；University of New South Wales（新南威尔士大学）；Nanyang Technological University（南洋理工大学）
DRL-Based Federated Self-Supervised Learning for Task Offloading and Resource Allocation in ISAC-Enabled Vehicle Edge Computing
School of Internet of Things Engineering, Jiangnan University（江南大学物联网工程学院）；Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology, Tsinghua University（清华大学信息科学与技术国家研究中心电子工程系）；State Key Lab. of ISN and School of Telecommunications Engineering, Xidian University（西安电子科技大学信息与通信系统国家重点实验室和电信工程学院）；Department of Electronic Engineering, Shanghai Jiao Tong University（上海交通大学电子工程系）；Department of Electrical and Computer Engineering, the Hong Kong University of Science and Technology (HKUST)（香港科技大学电子与计算机工程系）
Multi-task Domain Adaptation for Computation Offloading in Edge-intelligence Networks
School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机学院）；School of Cyber Engineering, Xidian University（西安电子科技大学网络与信息安全学院）；National and Kapodistrian University of Athens（雅典国立卡波迪斯特里亚大学）；University of Illinois Chicago（芝加哥大学伊利诺伊分校）；School of Electrical and Electronics Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）
