Zhenyu Tao

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 achieving service-oriented optimization goals through the selective acceptance of service requests. Although deep reinforcement learning (DRL) forms the foundation in many admission control approaches thanks to its effectiveness and flexibility, initial instability with excessive convergence delay of DRL models hinders their deployment in real-world networks. We propose a digital twin (DT) accelerated DRL solution to address this issue. Specifically, we first formulate the admission decision-making process as a semi-Markov decision process, which is subsequently simplified into an equivalent discrete-time Markov decision process to facilitate the implementation of DRL methods. A neural network-based DT is established with a customized output layer for queuing systems, trained through supervised learning, and then employed to assist the training phase of the DRL model. Extensive simulations show that the DT-accelerated DRL improves resource utilization by over 40% compared to the directly trained state-of-the-art dueling deep Q-learning model. This improvement is achieved while preserving the model's capability to optimize the long-term rewards of the admission process.

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 Gauss Markov model, can hardly capture the distribution characteristics of users within real-world areas. State-of-the-art trace-based mobility models and existing learning-based trajectory generation methods, however, are frequently constrained by the inaccessibility of substantial real trajectories due to privacy concerns. In this paper, we harness the intrinsic correlation between street maps and trajectories and develop a novel zero-shot trajectory generation method, named Map2Traj, by exploiting the diffusion model. We incorporate street maps as a condition to consistently pilot the denoising process and train our model on diverse sets of real trajectories from various regions in Xi'an, China, and their corresponding street maps. With solely the street map of an unobserved area, Map2Traj generates synthetic trajectories that not only closely resemble the real-world mobility pattern but also offer comparable efficacy. Extensive experiments validate the efficacy of our proposed method on zero-shot trajectory generation tasks in terms of both trajectory and distribution similarities. In addition, a case study of employing Map2Traj in wireless network optimization is presented to validate its efficacy for downstream applications.