102 citations · 133 across the 9 of their papers we have counts for
11 papers
Should I Use This Synthetic Dataset for Training? How to Test with Minimal Real Data
Zhenyu Tao, Wei Xu, Xiaohu You +2
Digital twins (DTs) and learned world models are increasingly used to generate synthetic data that augment the scarce real datasets available for training artificial intelligence (…
Intelligent Base Station Deployment in Urban Wireless Networks: A Geographic Data-Informed Digital Twin Approach
Zhenyu Tao, Yuxuan Li, Wei Xu +2
The placement of base station (BS) is a fundamental determinant of coverage and capacity of urban wireless networks. Yet large-scale BS deployment optimization remains challenging…
A Theoretical Analysis of State Similarity Between Markov Decision Processes
Zhenyu Tao, Wei Xu, Xiaohu You
The bisimulation metric (BSM) is a powerful tool for analyzing state similarities within a Markov decision process (MDP), revealing that states closer in BSM have more similar opti…
Toward Trustworthy Digital Twins in AI Agent-based Wireless Network Optimization: Challenges, Solutions, and Opportunities
Zhenyu Tao, Wei Xu, Xiaohu You
Optimizing modern wireless networks is exceptionally challenging due to their high dynamism and complexity. While the AI agent powered by reinforcement learning (RL) offers a promi…
A Generalized Bisimulation Metric of State Similarity between Markov Decision Processes: From Theoretical Propositions to Applications
Zhenyu Tao, Wei Xu, Xiaohu You
The bisimulation metric (BSM) is a powerful tool for computing state similarities within a Markov decision process (MDP), revealing that states closer in BSM have more similar opti…
Provable Performance Bounds for Digital Twin-driven Deep Reinforcement Learning in Wireless Networks: A Novel Digital-Twin Bisimulation Metric
Zhenyu Tao, Wei Xu, Xiaohu You
Digital twin (DT)-driven deep reinforcement learning (DRL) has emerged as a promising paradigm for wireless network optimization, offering safe and efficient training environment f…
Large Vision Model-Enhanced Digital Twin with Deep Reinforcement Learning for User Association and Load Balancing in Dynamic Wireless Networks
Zhenyu Tao, Wei Xu, Xiaohu You
Optimization of user association in a densely deployed cellular network is usually challenging and even more complicated due to the dynamic nature of user mobility and fluctuation…
Map2Traj: Street Map Piloted Zero-shot Trajectory Generation with Diffusion Model
Zhenyu Tao, Wei Xu, Xiaohu You
User mobility modeling serves a crucial role in analysis and optimization of contemporary wireless networks. Typical stochastic mobility models, e.g., random waypoint model and Gau…
Digital Twin Assisted Deep Reinforcement Learning for Online Admission Control in Sliced Network
Zhenyu Tao, Wei Xu, Xiaohu You
The proliferation of diverse wireless services in 5G and beyond has led to the emergence of network slicing technologies. Among these, admission control plays a crucial role in ach…
Wireless Network Digital Twin for 6G: Generative AI as A Key Enabler
Zhenyu Tao, Wei Xu, Yongming Huang +2
Digital twin, which enables emulation, evaluation, and optimization of physical entities through synchronized digital replicas, has gained increasing attention as a promising techn…
Deep Learning-Based Modeling of 5G Core Control Plane for 5G Network Digital Twin
Zhenyu Tao, Yongliang Guo, Guanghui He +2
Digital twin serves as a crucial facilitator in the advancement and implementation of emerging technologies within 5G and beyond networks. However, the intricate structure and dive…