Reinforcement Learning-Empowered Mobile Edge Computing for 6G Edge Intelligence-Reference-Cited by-同舟云学术

Reinforcement Learning-Empowered Mobile Edge Computing for 6G Edge Intelligence

Published:2022 Issue: Volume:10 Page:65156-65192
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Wei Peng¹^ORCID,Guo Kun²^ORCID,Li Ye³^ORCID,Wang Jue³^ORCID,Feng Wei¹^ORCID,Jin Shi⁴^ORCID,Ge Ning¹^ORCID,Liang Ying-Chang⁵^ORCID

Affiliation:

1. Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China

2. Shanghai Key Laboratory of Multidimensional Information Processing, School of Communications and Electronics Engineering, East China Normal University, Shanghai, China

3. School of Information Science and Technology, Nantong University, Nantong, China

4. National Mobile Communications Research Laboratory, Southeast University, Nanjing, China

5. National Key Laboratory on Communications, Center for Intelligent Networking and Communications, University of Electronic Science and Technology of China, Chengdu, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Natural Science Foundation of Tianjin

Science and Technology Development Fund of Tianjin Education Commission for Higher Education

Shanghai Municipal Science and Technology Major Project

Tsinghua University–China Mobile Communications Group Company Ltd. Joint Institute

Natural Science Foundation of Jiangsu Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09797722.pdf?arnumber=9797722

Reference185 articles.

1. Smart Resource Allocation for Mobile Edge Computing: A Deep Reinforcement Learning Approach

2. Joint Computation Offloading and Multiuser Scheduling Using Approximate Dynamic Programming in NB-IoT Edge Computing System

3. Multiuser Resource Control With Deep Reinforcement Learning in IoT Edge Computing

4. Deep-Reinforcement-Learning-Based Energy-Efficient Resource Management for Social and Cognitive Internet of Things

5. Resource Allocation in Software-Defined and Information-Centric Vehicular Networks with Mobile Edge Computing

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