Deep reinforcement learning aided secure UAV communications in the presence of moving eavesdroppers
Authors:
- Gongchao SuGSGongchao Su
- Mingjun DaiMDMingjun Dai
- Bin ChenBCBin Chen
- Xiaohui LinXLXiaohui Lin
Open Access
- 1 April 2024
- journal article
- research article
- Published by Springer Nature in Journal of King Saud University Computer and Information Sciences
- Vol. 36 (4)
Abstract
No abstract availableKeywords
This publication has 22 references indexed in Scilit:
- Double Deep Q-Learning With Prioritized Experience Replay for Anomaly Detection in Smart EnvironmentsIEEE Access, 2022
- What Will the Future of UAV Cellular Communications Be? A Flight From 5G to 6GIEEE Communications Surveys & Tutorials, 2022
- Robust 3D-Trajectory and Time Switching Optimization for Dual-UAV-Enabled Secure CommunicationsIEEE Journal on Selected Areas in Communications, 2021
- Deep Reinforcement Learning for Internet of Things: A Comprehensive SurveyIEEE Communications Surveys & Tutorials, 2021
- Q-Learning-Based Power Allocation for Secure Wireless Communication in UAV-Aided Relay NetworkIEEE Access, 2021
- Q-Learning Algorithms: A Comprehensive Classification and ApplicationsIEEE Access, 2019
- Applications of Deep Reinforcement Learning in Communications and Networking: A SurveyIEEE Communications Surveys & Tutorials, 2019
- Physical Layer Security for Next Generation Wireless Networks: Theories, Technologies, and ChallengesIEEE Communications Surveys & Tutorials, 2016
- Human-level control through deep reinforcement learningNature, 2015
- Simple statistical gradient-following algorithms for connectionist reinforcement learningMachine Learning, 1992