Xiaowen Ye
Orcid: 0000-0002-7047-0038
According to our database1,
Xiaowen Ye authored at least 23 papers
between 2017 and 2026.
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Bibliography
2026
Delay-Robust Deep Reinforcement Learning for Ranging-Free Channel Access under Mobility in Underwater Acoustic Networks.
CoRR, May, 2026
Intelligent Omni-Surface-Aided Multi-Objective ISAC: A Meta Hybrid Deep Reinforcement Learning Approach.
IEEE Trans. Mob. Comput., March, 2026
IEEE J. Biomed. Health Informatics, March, 2026
ISAC Beamforming for Underwater Acoustic Networks Based on Deep Reinforcement Learning.
IEEE Wirel. Commun. Lett., 2026
Integrated Sensing and Communications for Low-Altitude Economy: A Deep Reinforcement Learning Approach.
IEEE Trans. Wirel. Commun., 2026
2025
Leveraging Propagation Delays: A Delay-Aware Multiagent Reinforcement Learning MAC Protocol for Underwater Acoustic Networks.
IEEE Internet Things J., October, 2025
Intelligent Omni-Surface-Aided Integrated Sensing and Communications Based on Deep Reinforcement Learning With Knowledge Transfer.
IEEE Trans. Wirel. Commun., May, 2025
Digital-Twin-Enhanced Deep Reinforcement Learning for Intelligent Omni-Surface Configurations in MU-MIMO Systems.
IEEE Internet Things J., May, 2025
Joint MCS Adaptation and Beamforming Design for Multiuser MISO Systems: A Constrained Hybrid Deep Reinforcement Learning Approach.
IEEE Internet Things J., 2025
Energy-Efficient Link Adaptation for Underwater Acoustic Communications Based on Meta Deep Reinforcement Learning.
IEEE Internet Things J., 2025
2024
Joint Codebook Selection and MCS Adaptation for MmWave eMBB Services Based on Deep Reinforcement Learning.
IEEE Internet Things J., October, 2024
Joint Codebook Selection and UE Scheduling for Unlicensed MmWave NR-U/WiGig Coexistence Based on Deep Reinforcement Learning.
IEEE Trans. Mob. Comput., September, 2024
Deep Reinforcement Learning-Based Scheduling for NR-U/WiGig Coexistence in Unlicensed mmWave Bands.
IEEE Trans. Wirel. Commun., January, 2024
Joint MCS Adaptation and RB Allocation in Cellular Networks Based on Deep Reinforcement Learning With Stable Matching.
IEEE Trans. Mob. Comput., January, 2024
2023
IEEE Trans. Veh. Technol., June, 2023
MAC Protocol for Underwater Acoustic Multi-Cluster Networks Based on Multi-Agent Reinforcement Learning.
Proceedings of the 17th International Conference on Underwater Networks & Systems, 2023
2022
Multi-Channel Opportunistic Access for Heterogeneous Networks Based on Deep Reinforcement Learning.
IEEE Trans. Wirel. Commun., 2022
IEEE Trans. Mob. Comput., 2022
Deep Reinforcement Learning Based Scheduling Scheme for the NR-U/WiGig Coexistence in Unlicensed mmWave Bands.
Proceedings of the IEEE International Conference on Communications, 2022
2021
Modified AC/DC Unified Power Flow and Energy-Saving Evaluation for Urban Rail Power Supply System With Energy Feedback Systems.
IEEE Trans. Veh. Technol., 2021
2020
The optimal network throughputs when the model-aware node coexists with other nodes using different MAC protocols.
CoRR, 2020
MAC Protocol for Multi-channel Heterogeneous Networks Based on Deep Reinforcement Learning.
Proceedings of the IEEE Global Communications Conference, 2020
2017
Optimal design of inverter feedback device for urban rail traction power supply system.
Proceedings of the IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society, Beijing, China, October 29, 2017