叶小文
副教授
E-mail: xiaowenye@fjnu.edu.cn
个人简介
叶小文（1996.10），博士（硕博连读）毕业于厦门大学，曾在香港城市大学从事博士后研究工作，现任福建师范大学光电与信息工程学院网络与通信工程系副教授。长期致力于智能无线通信、水声通信组网、通感一体化等领域的研究，近五年以第一作者在IEEE Transactions on Wireless Communications、IEEE Transactions on Mobile Computing等高水平国际期刊上发表相关论文十余篇，以第一、第二作者在IEEE GLOBECOM、IEEE ICC等高水平国际学术会议上发表相关论文5篇并作学术报告，以第二、第三发明人取得授权专利2项。此外，与国内外众多学者保持着密切的学术交流，并携手合作开展了多项研究工作；担任IEEE Journal on Selected Areas in Communications, IEEE Transactions on Wireless Communications, IEEE Transactions on Mobile Computing, IEEE Transactions on Communications, IEEE Transactions on Cognitive Communications and Networking等高水平国际期刊的审稿人。
研究方向
智能无线通信、水声通信组网、通感一体化等。
科研项目
1. 国家自然科学基金青年科学基金项目（C类），30万，2026.01--2028.12， 在研，主持。
2. 福建省第一批青年科技人员育成项目（A类），3万，2026.01--2027.12， 在研，主持。
3. 福建师范大学人才引进科研基金项目，50万，2025--至今，在研，主持。
4. 国家自然科学基金区域创新发展联合基金项目，262万，2026.01--2029.12，在研，第一参与人。
代表性论文
1.智能无线通信，已发表相关论文如下：
[1] Xiaowen Ye, Qian Zhou, and Liqun Fu. Deep reinforcement learning based scheduling for NR-U/WiGig coexistence in unlicensed mmWave bands[J]. IEEE Transactions on Wireless Communications. 2023, 23(1): 58-73.（中科院一区TOP）
[2] Xiaowen Ye, Yiding Yu, and Liqun Fu. Multi-channel opportunistic access for heterogeneous networks based on deep reinforcement learning[J]. IEEE Transactions on Wireless Communications, 2021, 21(2): 794-807.（中科院一区TOP）
[3] Xiaowen Ye and Liqun Fu. Joint codebook selection and UE scheduling for unlicensed mmWave NR-U/WiGig coexistence based on deep reinforcement learning[J]. IEEE Transactions on Mobile Computing, 2024, 23(9), 8919-8934.（CCF A刊，中科院二区TOP）
[4] Xiaowen Ye and Liqun Fu. Joint MCS adaptation and RB allocation in cellular networks based on deep reinforcement learning with stable matching[J]. IEEE Transactions on Mobile Computing, 2022, 23(1): 549-565.（CCF A刊，中科院二区TOP）
[5] Xiaowen Ye, Xianghao Yu, and Liqun Fu. Digital twin enhanced deep reinforcement learning for intelligent omni-surface configurations in MU-MIMO systems[J]. IEEE Internet of Things Journal, 2025, 12(9) 13005-13020.（中科院一区TOP）
[6] Xiaowen Ye, Yuyi Mao, Xianghao Yu, and Liqun Fu. Joint MCS adaptation and beamforming design for multi-user MISO systems: A constrained hybrid deep reinforcement learning approach[J]. IEEE Internet of Things Journal, Early Access, 2025.（中科院二区TOP）
[7] Xiaowen Ye, Liqun Fu, and John M. Cioffi. Joint codebook selection and MCS adaptation for mmWave eMBB services based on deep reinforcement learning[J]. IEEE Internet of Things Journal, 2024, 11(19), 31545-31560.（中科院一区TOP）
[8] Xiaowen Ye, Yiding Yu, and Liqun Fu. Deep reinforcement learning based link adaptation technique for LTE/NR systems[J]. IEEE Transactions on Vehicular Technology, 2023, 72(6): 7364-7379.（中科院二区TOP）
[9] Xiaowen Ye, Yiding Yu, and Liqun Fu. MAC protocol for multi-channel heterogeneous networks based on deep reinforcement learning[C]. in Proc. IEEE Global Communications Conference (GLOBECOM), 2020: 1-6.（EI 会议论文，CCF C会，无线通信领域旗舰会议）
[10] Qian Zhou, Xiaowen Ye, and Liqun Fu. Deep reinforcement learning based scheduling scheme for the NR-U/WiGig coexistence in unlicensed mmWave bands[C]. in Proc. IEEE International Conference on Communications (ICC), 2022: 4468-4473. （EI 会议论文，CCF C会，无线通信领域旗舰会议）
2.水声通信组网，已发表相关论文如下：
[11] Xiaowen Ye, Yiding Yu, and Liqun Fu. Deep reinforcement learning based MAC protocol for underwater acoustic networks[J]. IEEE Transactions on Mobile Computing, 2020, 21(5): 1625-1638.（CCF A刊，中科院二区TOP）
[12] Xiaowen Ye, Liqun Fu, Xianxin Song, and Yi Wu. Energy-efficient link adaptation for underwater acoustic communications based on meta deep reinforcement learning[J]. IEEE Internet of Things Journal, 2025, 12(22): 46648-46660.（中科院二区TOP）
[13] Xiaowen Ye and Liqun Fu. Deep reinforcement learning based MAC protocol for underwater acoustic networks [C]. in Proc. ACM International Conference on Underwater Networks & Systems (WUWNet) , 2019: 1-5. （EI会议论文，水声通信领域旗舰会议）
[14] Jiajie Huang, Xiaowen Ye, Yizhe Wang, Liqun Fu. Leveraging propagation delays: A delay-aware multi-agent reinforcement learning MAC protocol for underwater acoustic networks[J]. IEEE Internet of Things Journal, Early Access, 2025.（中科院二区TOP）
[15] Jiajie Huang, Xiaowen Ye, and Liqun Fu. MAC protocol for underwater acoustic multicluster networks based on multi-agent reinforcement learning[C]. in Proc. ACM International Conference on Underwater Networks & Systems (WUWNet), 2023: 1-5.（EI会议论文，水声通信领域旗舰会议）
[16] Zewen Zhu, Xiaowen Ye, and Liqun Fu. Deep reinforcement learning based energy efficient underwater acoustic communications[C]. in Proc. IEEE Global Oceans (OCENAS), 2020: 1-5.（EI会议论文，水声通信领域国际旗舰会议）
3.通感一体化，已发表相关论文如下：
[17] Xiaowen Ye, Yuyi Mao, Xianghao Yu, and Liqun Fu. Intelligent omni-surface-aided integrated sensing and communications based on deep reinforcement learning with knowledge transfer[J]. IEEE Transactions on Wireless Communications, 2025, 24(5): 4344-4360.（中科院一区TOP）
[18] Xiaowen Ye, Yuyi Mao, Xianghao Yu, Shu Sun, Liqun Fu, and Jie Xu. Integrated sensing and communications for low-altitude economy: A deep reinforcement learning approach[J]. IEEE Transactions on Wireless Communications, Early Access, 2025.（中科院一区TOP）
[19] Xiaowen Ye, Xianxin Song, Yi Wu, and Liqun Fu. Intelligent omni-surface-aided multi-objective ISAC: A meta hybrid deep reinforcement learning approach[J]. IEEE Transactions on Mobile Computing, Early Access, 2025.（CCF A刊，中科院一区TOP）
