The following publications are possibly variants of this publication:
- UAV-Aided Anti-Jamming Maritime Communications: A Deep Reinforcement Learning ApproachKexin Liu, Pengmin Li, Chuhuan Liu, Liang Xiao 0003, Luliang Jia. wcsp 2021: 1-6 [doi]
- Joint Power and Channel Selection for Anti-jamming Communications: A Reinforcement Learning ApproachXufang Pei, Ximing Wang, Lang Ruan, Luying Huang, Xingyue Yu, Heyu Luan. mlicom 2019: 551-562 [doi]
- Joint Time-frequency Anti-jamming Communications: A Reinforcement Learning ApproachXufang Pei, Ximing Wang, Junnan Yao, Changhua Yao, Jincheng Ge, Luying Huang, Dianxiong Liu. wcsp 2019: 1-6 [doi]
- Intelligent Dynamic Spectrum Anti-Jamming Communications: A Deep Reinforcement Learning PerspectiveWen Li, Jin Chen 0007, Xin Liu 0021, Ximing Wang, Yangyang Li, Dianxiong Liu, Yuhua Xu 0001. wc, 29(5):60-67, 2022. [doi]
- Towards reinforcement learning in UAV relay for anti-jamming maritime communicationsChuhuan Liu, Yi Zhang 0035, Guohang Niu, Luliang Jia, Liang Xiao, Jiangxia Luan. dcan, 9(6):1477-1485, December 2023. [doi]
- Mean Field Reinforcement Learning Based Anti-Jamming Communications for Ultra-Dense Internet of Things in 6GXiming Wang, Yuhua Xu 0001, Jin Chen 0007, Chunguo Li, Xin Liu 0021, Dianxiong Liu, Yifan Xu 0003. wcsp 2020: 195-200 [doi]
- Dynamic Spectrum Anti-Jamming in Broadband Communications: A Hierarchical Deep Reinforcement Learning ApproachYangyang Li, Yuhua Xu 0001, Yitao Xu, Xin Liu 0021, Ximing Wang, Wen Li, Alagan Anpalagan. wcl, 9(10):1616-1619, 2020. [doi]
- Mean-Field Multi-Agent Reinforcement Learning for Adaptive Anti-Jamming Channel Selection in UAV CommunicationsFeng Du, Jun Li, Yan Lin, Zhe Wang, Yuwen Qian. wcsp 2022: 910-915 [doi]