Wen Wu
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Selected Publications (# marks the corresponding author)

Note that the full list can be found in [here]. IEEE Copyright: Personal use of the materials is permitted, but republication/redistribution requires IEEE permission.

Books

  1. W. Wu, S. Zhang, P. Dong, and X. Shen (authors), “Collaborative Edge AI over 6G Networks,” Springer Verlag, proposal approved, 2026.

  2. P. Yang, W. Wu, N. Zhang, and X. Shen (authors), “Millimeter-Wave Networks: Beamforming Design and Performance Analysis,” Springer Verlag, 2021. (ISBN-10: 3030886298, ISBN-13: 9783030886295) [pdf]

  3. W. Wu, Y. Tang, P. Yang, W. Zhang, and N. Zhang, “Collaborative Deep Neural Network Inference via Mobile Edge Computing,” Broadband Communications, Computing, and Control for Ubiquitous Intelligence, pp. 263-190. Editors: L. Cai, B. L. Mark, and J. Pan, Springer, 2022. [pdf]

Preprint

  1. [arXiv] M. Guo, W. Wu#, Y. Wang, and S. Zhang, “Large-Small Model Collaboration for LEO Satellite Networks: An Agentic AI Approach,” IEEE Transactions on Cognitive Communications and Networking, under revision.

  2. [arXiv] Y. Wang, W. Wu#, K. Qu, and X. Shen, “Edge-Accelerated Cooperative Sensing over CAV Networks: A Transformer-Based RL Approach,” IEEE Transactions on Mobile Computing, submission.

  3. [arXiv] S. Zhang, W. Wu#, L. Li, Y. Wang, and X. Shen, “Communication-Efficient Collaborative LLM Inference over LEO Satellite Networks,” IEEE Journal on Selected Areas in Communications, submission, 2025.

  4. [arXiv] Z. Li, W. Wu#, S. Wu, and X. Shen, “Fast AI Model Partition for Split Learning over Edge Networks,” IEEE Transactions on Mobile Computing, under revision, 2025.

  5. [arXiv] Z. Mao, W. Wu#, and X. Shen, “Learning-Based Radio Resource Management and Bitrate Adaptation for Multi-UAV Video Streaming over Low-Altitude Wireless Networks,” IEEE Journal of Selected Topics in Signal Processing, under revision, 2025.

  6. [arXiv] K. Shang, W. Wu#, J. Tang, Z. Mao, and X. Shen, “Energy-Efficient RAN Slicing via Parameterized Deep Reinforcement Learning,” IEEE Transactions on Cognitive Communications and Networking, minor revision, 2025.

  7. [arXiv] J. Cong, W. Wu#, C. You, J. Huang, and Z. Mao, “Data Generation for Heterogeneous Federated Learning in Wireless Networks via Diffusion Model,” IEEE Transactions on Network Science and Engineering, revision, 2026.

Journal

  1. [TRO] B. Liu, Q. Zhang, Y. Lu, J. Jiao, C. Jagmohan, W. Wu, J. Zhang, and K. Dimitrios, “MobileROS: A Wireless-Native Robot Operating System for Mobile Robotics,” IEEE Transactions on Robotics, to appear, 2026. （CCF A，中科院1区Top）

  2. [TWC] W. Jiang, B. Ai, W. Wu#, L. Qian, and L. Liu, “Energy-Efficient Aerial IRS Configuration and Resource Allocation in AoI-Aware MEC,” IEEE Transactions on Wireless Communications, to appear, 2026. [pdf]

  3. [TMC] H. Tu, W. Wu#, L. Chen, L. Li, X. Chen, and X. Shen, “FL in Motion: Accelerating FL via Mobility-Aware Vehicle Selection and Sparse Training,” IEEE Transactions on Mobile Computing, to appear, 2026. [pdf]

  4. [Network] W. Wu, X. Huang, M. Qin, Q. Li, N. Cheng, and T. H. Luan, “AI-Native Network Digital Twin for Intelligent Network Management in 6G,” IEEE Network, to appear, 2026. [pdf]

  5. [COMNET’26] H. Tu, W. Wu#, L. Li, Y. Lu, L. Chen, and X. Chen, “V-FedMM: Dynamic Sample Selection for Efficient Multimodal Federated Learning over Vehicular Networks,” Computer Networks (Elsevier), to appear, 2026. [pdf]

  6. [TCCN’26] L. Li, W. Wu#, S. Fang, S. Wang, and C. Yin, “Wider Eyes in the Sky: Scene-Adaptive Video Analytics in Low-Altitude Surveillance Networks,” IEEE Transactions on Cognitive Communications and Networking, vol. 12, pp. 4624 - 4639, 2026. [pdf]

  7. [TNSE’26] X. Cheng, W. Wu#, Y. Wang, Z. Mao, Y. Lu, and P. Dong, “Dependency-Aware CAV Task Scheduling via Diffusion-Based Reinforcement Learning,” IEEE Transactions on Network Science and Engineering, vol. 13, pp. 4797 - 4814, 2026. [pdf]

  8. [IoTJ’25] X. Chen, W. Wu#, F. Ji, Y. Lu, and L. Li, “Privacy-Aware SFL for Resource-Efficient LLM Fine-Tuning over IoT Devices,” IEEE Internet of Things Journal, vol. 12, no. 24, pp. 51902 - 51913, Dec. 2025. [pdf]

  9. [JSTSP’25] L. Li, X. Yang, W. Wu#, H. Wang, T. Ohtsuki, M. Pan, and X. Shen, “MobiLLM: Enabling On-Device Fine-Tuning of Billion-Sized LLMs via Server-Assisted Side-Tuning,” IEEE Journal of Selected Topics in Signal Processing, vol. 19, no. 7 pp. 1251 - 1265, 2025. [pdf]

  10. [JSTSP’25] S. Zhang, G. Cheng, W. Wu#, X. Huang, L. Song, and X. Shen, “Split Fine-Tuning for Large Language Models in Wireless Networks,” IEEE Journal of Selected Topics in Signal Processing, vol. 19, no. 7, pp. 1376 - 1391, Oct. 2025. [pdf]

  11. [SCIS’25] W. Wu, and X. Huang, “Split-LEO: Efficient AI Model Training over LEO Satellite Networks,” SCIENCE CHINA Information Sciences, vo. 68, no. 190305, 2025. [pdf]

  12. [IoTM’25] X. Chen, W. Wu#, L. Li and F. Ji, “LLM-Empowered IoT for 6G Networks: Architecture, Challenges, and Solutions,” IEEE Internet of Things Magazine, vol. 8, no. 6, pp. 34-41, Nov. 2025. [pdf]

  13. [TMC’25] S. Zhang, W. Wu#, L. Song, and X. Shen, “Efficient Model Training in Edge Networks with Hierarchical Split Learning,” IEEE Transactions on Mobile Computing, vol. 24, no. 10, pp. 10214 - 10229, Oct. 2025. [pdf]

  14. [IoTM’25] Z. Li, S. Wu, W. Wu#, Q. Lin, Y. Sun, and H. Wang, “Split Knowledge Distillation for Large Models in IoT: Architecture, Challenges, and Solutions,” IEEE Internet of Things Magazine, vol. 8, no. 5, pp. 16 - 23, Sep. 2025. [pdf]

  15. [TWC’24] K. Qu, W. Zhuang, Q. Ye, W. Wu#, and X. Shen, “Model-Assisted Learning for Adaptive Cooperative Perception of Connected Autonomous Vehicles,” IEEE Transactions on Wireless Communications, vol. 23, no. 8, pp. 8820-8835, Aug. 2024. [pdf]

  16. [JSTSP’24] X. Huang, W. Wu, S. Hu, M. Li, C. Zhou, and X. Shen, “Digital Twin Based User-Centric Resource Management for Multicast Short Video Streaming”, IEEE Journal of Selected Topics in Signal Processing, vol. 18, no. 1, pp. 50-65, Jan. 2024. [pdf]

  17. [JSAC’23] W. Wu, M. Li, K. Qu, C. Zhou, X. Shen, W. Zhuang, X. Li, and W. Shi, ”Split Learning over Wireless Networks: Parallel Design and Resource Management,” IEEE Journal on Selected Areas in Communications, vol. 41, no. 4, pp. 1051-1066, Apr. 2023. [pdf] （ESI Highly Cited Paper）

  18. [IoTJ’23] K. Qu, W. Zhuang, W. Wu#, M. Li, X. Shen, X. Li, and W. Shi, “Stochastic Cumulative DNN Inference with RL-Aided Adaptive IoT Device-Edge Collaboration,” IEEE Internet of Things Journal, vol. 10, no. 20, pp. 18000 - 18015, Oct. 2023. [pdf]

  19. [WCM’22] W. Wu, C. Zhou, M. Li, H. Wu, H. Zhou, N. Zhang, X. Shen, and W. Zhuang, “AI-Native Network Slicing for 6G Networks,” IEEE Wireless Communications, vol. 29, no. 1, pp. 96–103, Feb. 2022. [pdf] （ESI Highly Cited Paper）

  20. [COMST’22] X. Shen, J. Gao, W. Wu, M. Li, C. Zhou, and W. Zhuang, “Holistic Network Virtualization and Pervasive Network Intelligence for 6G,” IEEE Communications Surveys and Tutorials, vol. 24, no. 1, pp. 1-30, 1st. Quart. 2022. [pdf] （Editor-in-Chief Invited Paper, Cover Paper, ESI Highly Cited Paper）

  21. [TII’21] W. Wu, P. Yang, W. Zhang, C. Zhou, and X. Shen, “Accuracy-Guaranteed Collaborative DNN Inference in Industrial IoT via Deep Reinforcement Learning,” IEEE Transactions on Industrial Informatics, vol. 17, no. 7, pp. 4988–4998, July 2021. [pdf]

  22. [JSAC’21] W. Wu, N. Chen, C. Zhou, M. Li, X. Shen, W. Zhuang, and X. Li, “Dynamic RAN Slicing for Service-Oriented Vehicular Networks via Constrained Learning,” IEEE Journal on Selected Areas in Communications, vol. 39 no. 7, pp. 2076–2089, July 2021. [pdf]

  23. [TWC’21] C. Zhou, W. Wu, H. He, P. Yang, F. Lyu, N. Cheng, and X. Shen, “Deep Reinforcement Learning for Delay-Oriented IoT Task Scheduling in Space-Air-Ground Integrated Network,” IEEE Transactions on Wireless Communications, vol. 20, no. 2, pp. 911-925, Feb. 2021. [pdf] （ESI Highly Cited Paper）

  24. [OJVT’20] X. Shen, J. Gao, W. Wu, K. Lyu, M. Li, W. Zhuang, X. Li, and J. Rao, “AI-assisted Network-slicing based Next-generation Wireless Networks,” IEEE Open Journal of Vehicular Technology, vol. 1, no. 1, pp. 45-66, Jan. 2020. [pdf] （Editor-in-Chief Invited Paper, IEEE Vehicular Technology Society OJVT Best Paper Award）

  25. [TVT’20] W. Wu, N. Cheng, N. Zhang, P. Yang, K. Aldubaikhy, and X. Shen, “Performance Analysis and Enhancement of Beamforming Training in 802.11ad,” IEEE Transactions on Vehicular Technology, vol. 69, no. 5, pp. 5293-5306, May 2020. [pdf]

  26. [TWC’19] W. Wu, N. Cheng, N. Zhang, P. Yang, W. Zhuang, and X. Shen, “Fast mmwave Beam Alignment via Correlated Bandit Learning,” IEEE Transactions on Wireless Communications, vol. 18, no. 12, pp. 5894-5908, Dec. 2019. [pdf]

  27. [TVT’19] W. Wu, N. Zhang, N. Cheng, Y. Tang, K. Aldubaikhy, and X. Shen, “Beef up mmWave Dense Cellular Networks with D2D-Assisted Cooperative Edge Caching,” IEEE Transactions on Vehicular Technology, vol. 68, no. 4, pp. 3890-3904, Apr. 2019. [pdf]

Conference

  1. [ICDCS’26] G. Cheng, W. Wu#, J. Huang, J. Cong, Z. Mao, and C. You, “DCDR: Device–Cloud Collaborative Framework for Transformer-Based Intelligent Signal Recognition,” in Proc. IEEE International Conference on Distributed Computing Systems (ICDCS) PhD Student Symposium, Seoul, South Korea, June 22–25, 2026.

  2. [ICDCS’26] X. Cao, W. Wu#, L. Li, and C. She, “AI Agent-Driven Closed-Loop Control for Integrated Sensing and Communication Systems,” in Proc. IEEE International Conference on Distributed Computing Systems (ICDCS) PhD Student Symposium, Seoul, South Korea, June 22–25, 2026.

  3. [ICCT’25] K. Shang, W. Wu#, J. Tang, J. Cong, Z. Mao, and X. Shen, “Adaptive RAN Slicing for Diffusion-based AIGC Services in Mobile Edge Networks,” in Proc. IEEE ICCT, Shenyang, China, Oct. 16-18, 2025. (Best Student Paper Award )

  4. [Mobicom Workshop’25] L. Li, and W. Wu#, “Memory-Efficient LLM Fine-Tuning on the Mobile Device via Server Assisted Side Tuning,” in Proc. ACM Mobicom Workshop, Hong Kong, Nov. 4-8, 2025. [pdf] (Mobicom Workshop Best Paper Award )

  5. [ICDCS’24] Y. Lu and W. Wu#, “Digital Twin Assisted Cross-Layer Resource Scheduling in ORAN System,” in Proc. IEEE ICDCS PhD Student Symposium, New Jersey, USA, July 23-26, 2024. [pdf]

  6. [USENIX Security’24] S. Li, X. Wang, M. Xue, H. Zhu, Z. Zhang, Y. Gao, W. Wu, and X. Shen, “Yes, One-Bit-Flip Matters! Universal DNN Model Inference Depletion with Runtime Code Fault Injection,” in Proc. USENIX Security (Security), Philadelphia, USA, August 14–16, 2024. [pdf] （Distinguished Paper Award ）

  7. [ICDCS’23] X. Huang, W. Wu#, and X. Shen, “Digital Twin-Assisted Resource Demand Prediction for Multicast Short Video Streaming,” in Proc. IEEE International Conference on Distributed Computing Systems (ICDCS) PhD Student Symposium, Hong Kong, China, July 18-21, 2023. [pdf]

  8. [ICDCS’23] S. Zhang, W. Wu#, P. Hu, S. Li, and N. Zhang, “Split Federated Learning: Speed up Model Training in Resource-Limited Wireless Networks,” in Proc. IEEE International Conference on Distributed Computing Systems (ICDCS) PhD Student Symposium, Hong Kong, China, July 18-21, 2023. [pdf]

  9. [ICCC’22] W. Wu, K. Qu, P. Yang, N. Zhang, X. Shen, and W. Zhuang, “Cost-Effective Two-Stage Network Slicing for Edge-Cloud Orchestrated Vehicular Networks,” in Proc. IEEE/CIC International Conference on Communications in China (ICCC), Foshan, China, 2022. [pdf] (Best Paper Award )

Patents

  1. Inventors: X. Shen, W. Wu, M. Li, K. Qu, C. Zhou, W. Zhuang, and X. Li, “Systems and Methods for Cluster-Based Parallel Split Learning”. United States, App. 18,884,763, 2024/09/13. Patent Status: Granted/Issued. Year Issued: 2025/01/02. [pdf]

  2. Inventors: X. Shen, W. Wu, M. Li, K. Qu, C. Zhou, W. Zhuang, and X. Li, “Systems and Methods for Cluster-Based Parallel Split Learning”. Canada, International application number: PCT/CA2022/050487, 2022/03/30. Patent Status: Pending.

  3. Inventors: L. Li, W. Wu, “A Memory-Efficient System and Method for Fine-Tuning Large Models on Edge Devices.” United States, PCT/CN2024/136837, 2024/12/16. Patent Status: Pending.

  4. Inventors: L. Li, W. Wu, X. Chen, “Systems and Methods for Collaborative Fine-Tuning Large Models Based on On-Demand Parameter Activation.” United States, PCT/CN2024/129871, 2024/11/5. Patent Status: Pending.

  5. Inventors: W. Wu, K. Shang, J. Tang, Z. Mao, “Systems and Methods for Energy-Efficient Network Slicing Switch and Resource Management .” United States, PCT/CN2025/096426, 2025/5/22. Patent Status: Pending.