Qianren Li (李乾任) received his Master and Bachelor degree from Southern University of Science and Technology (SUSTech) under the supervision of Professor Rui Wang. He is currently a research assistant of the LASSO lab in SUSTech.

His research focuses on the intersection of wireless communication, optimization and machine learning, particularly the development and implementation of machine-learning-enabled communication systems.

His current project involves optimization in WiFi systems.

Skills

• Programming: Python, RUST, MATLAB，Labview, C, TypeScript
• Math Tools: Markov Decision Process, Convex Optimization
• Software: Linux, Pytorch, Docker, Git, Overleaf, VSCode, Solidworks, AutoCAD, HFSS, ADS, and STM32CubeIDE

Educations

• M.S., Southern University of Science and Technology, Electronics and Electrical Engineering. 2022.09 - 2025.06

• B.S., Southern University of Science and Technology, Communication Engineering. 2018.09 - 2022.07

Publications

• Qianren Li, Yuncong Hong, Bojie Lv, and Rui Wang, “A Dynamic Programming Framework for Vehicular Task Offloading with Successive Action Improvement,” in IEEE Transactions on Communications, doi: 10.1109/TCOMM.2025.3610216.
• Qianren Li, Bojie Lv, Yuncong Hong, and Rui Wang,, “ReinWiFi: Application-Layer QoS Optimization of WiFi Networks with Reinforcement Learning,” 2025 IEEE 101st Vehicular Technology Conference (VTC2025-Spring), Oslo, Norway, 2025, pp. 1-6, doi: 10.1109/VTC2025-Spring65109.2025.11174355.
• Qianren Li, Yuncong Hong, Bojie Lv, and Rui Wang, “A Dynamic Improvement Framework for Vehicular Task Offloading”. arXiv preprint arXiv:2501.11333. (Accepted by IEEE Wireless Communications and Networking Conference)
• Bojie Lv, Qianren Li and Rui Wang, “Sensing-Assisted Adaptive Channel Contention for Mobile Delay-Sensitive Communications”, 2024, arXiv:2405.06186. (Accepted by IEEE Global Communications Conference)

Project Experience

Reinwifi 2023.06 - now

• Description: Extracting information from the media access control (MAC) layer in real time is either infeasible or hampers transmission efficiency. To address this issue, a reinforcement-learning-based framework for application-layer quality-of-service (QoS) optimization of WiFi networks was proposed.
• Role: Project Leader, Developer
• Tools: Python, Pytorch, C, RUST

InertiEAR-Implementation 2022.12 - 2023.1

• Description: Speakers in cell phones might induce vibration in embedded inertial measurement units (IMU); therefore, utilizing IMU data to infer victims’ sensitive vocal information is possible.(Course Project)
• Role: Project Leader, Developer
• Tools: Python, Pytorch

• Overleaf-Workshop 2023.7 - now

  • Description: Open Overleaf/ShareLaTex projects in vscode, with full collaboration support.
  • Role: Developer, Maintainer
  • Tools: Typescript, Node.js