Jingxuan Men

Postgraduate Research Student in Semantic Communications


My research project

My qualifications

Research Assistant
The Hang Seng University of Hong Kong
MSc in Telecommunications
The Hong Kong University of Science and Technology


Research interests


Jingxuan Men, Yun Hou (2022) Controlled Mobility for C-V2X Road Safety Reception Optimization

The use case of C-V2X for road safety requires real-time network connection and information exchanging between vehicles. In order to improve the reliability and safety of the system, intelligent networked vehicles need to move cooperatively to achieve network optimization. In this paper, we use the C-V2X sidelink mode 4 abstraction and the regression results of C-V2X network level simulation to formulate the optimization of packet reception rate (PRR) with fairness in the road safety scenario. Under the optimization framework, we design a controlled mobility algorithm for the transmission node to adaptively adjust its position to maximize the aggregated PRR using only one-hop information. Simulation result shows that the algorithm converges and improve the aggregated PRR and fairness for C-V2X mode broadcast messages.

Jingxuan Men, Yun Hou, Zhengguo Sheng, Tse-Tin Chan (2023) Enhancing C-V2X Network Connectivity with Distributed Mobility Control

The high mobility feature of vehicular networks poses tremendous challenges to maintaining network connectivity. In this paper, we investigate the possibility of enhancing the connectivity of Cellular Vehicle-to-Everything (C-V2X) networks through distributed trajectory adjustment. Based on a physical layer abstraction model, we characterize the network connectivity enhancement problem as a network utility maximization and study its concavity. We propose a distributed trajectory updating algorithm that dynamically adjusts the trajectory of vehicles on top of their planned trajectory. The algorithm is distributed and requires only geo-location exchanges, which are readily available in V2X networks. Simulation results show that the mobility updating algorithm converges and improves the aggregated network utility by up to 48% compared to the scenarios without mobility tuning.