Wang, Fangxin

Deep learning for edge computing applications: A State-of-the-Art survey

Wang, F., Zhang, M., Wang, X., Ma, X., & Liu, J. (2020) · IEEE Access

IoT and Edge/Fog ComputingAdvanced Neural Network Applications

Cited by 259

NetLLM: adapting large language models for networking

Wu, D., Wang, X., Qiao, Y., Wang, Z., Jiang, J., Cui, S., & Wang, F. (2024) · Proceedings of the ACM SIGCOMM 2024 Conference

Software-Defined Networks and 5GDomain Adaptation and Few-Shot Learning

Cited by 167

Intelligent Edge-Assisted Crowdcast with Deep Reinforcement Learning for Personalized QoE

Wang, F., Zhang, C., wang, F., Liu, J., Zhu, Y., Pang, H., & Sun, L. (2019) · IEEE INFOCOM 2019 - IEEE Conference on Computer Communications

IoT and Edge/Fog ComputingImage and Video Quality Assessment

Cited by 87

Backup Battery Analysis and Allocation against Power Outage for Cellular Base Stations

Wang, F., Fan, X., Wang, F., & Liu, J. (2019) · IEEE Transactions on Mobile Computing

Battery Modeling and ManagementReliability and Maintenance Optimization

Cited by 41

Task Scheduling with Optimized Transmission Time in Collaborative Cloud-Edge Learning

Huang, Y., Zhu, Y., Fan, X., Ma, X., Wang, F., Liu, J., Wang, Z., & Cui, Y. (2018) · 2018 27th International Conference on Computer Communication and Networks (ICCCN)

IoT and Edge/Fog ComputingCloud Computing and Resource Management

Cited by 36

When Users Change Their Mind: Evaluating Interruptible Agents in Long-Horizon Web Navigation

Zou, H. P., Miao, C., Huang, W.-C., Chen, Y., Zhou, Y., Zhang, H., Wu, Y., Fang, L., Gu, Z., Zhang, Z., et al. (2026) · arXiv

Information Retrieval and Search BehaviorAI-based Problem Solving and Planning

Cited by 0