Chi Weiqi

Chi Weiqi received her B.E. degree in information engineering from Tianjin University of Finance and Economics, China, in 2020, and an M.E. degree in wireless communication engineering from Tokyo Institute of Technology, Japan, in 2022. She is currently pursuing a Ph.D. degree in information engineering at the Graduate School of Information Science and Technology, The University of Tokyo, Japan.

From November 2024 to January 2025, she was a Visiting Researcher at Technische Hochschule Ingolstadt, Germany, collaborating with the Artery core development team at the Car2X-Laboratory. She also has hands-on Over-the-Air (OTA) testing experience for Internet Connected Vehicles (ICVs) at General Test Systems Inc., China (2023), including anechoic chamber work and wireless test instrumentation.

Her research interests include mmWave vehicular networks, machine learning for wireless communication, user association optimization, V2X communication, intelligent transportation systems (ITS), and VANET simulation. Her current Ph.D. research focuses on user association in mmWave V2X networks based on machine learning, particularly developing contextual multi-armed bandit (CMAB) based algorithms that address non-stationary reward problems in dynamic vehicular environments.

She is a project student of the JST SPRING GX program since April 2024 and was awarded the Young Researcher’s Encouragement Award by the IEEE VTS Tokyo/Japan Chapter in 2024. She was also a project student member of Tokyo Institute of Technology’s Super Smart Society (SSS) program from 2021 to 2022.

selected publications

International Conf.
Fast-Adapting V2X Networks: A Dynamic Context Clustering Multi-Armed Bandits

Weiqi Chi, Xiaoyang He, and Manabu Tsukada

In IEEE Vehicular Networking Conference (VNC2026), 2026

Abs Bib PDF

While contextual multi-armed bandit (CMAB) effectively addresses non-stationary reward for problems such as millimeter-wave (mmWave) Vehicle-to-Everything (V2X) user association (UA), it suffers from slow convergence when dealing with a large-scale context space. This paper proposes a Dynamic Context Clustering Semi-distributed Multi-Armed Bandit (DCC-SMAB) algorithm for the UA problem in mmWave vehicular networks that dynamically clusters vehicle positions as contexts, thereby accelerating convergence in reward estimation without requiring additional channel state information (CSI) compared to the CMAB framework with fixed context partitioning. The algorithm exhibits strong capability in identifying mmWave channel variations, enabling rapid adaptation to non-stationary communication environments for both newly arriving and existing vehicles in the network. Extensive simulations demonstrate over 61% and 76% in average regret reduction and network transmission rate improvement compared to benchmarks using Upper Confidence Bound (UCB) policy, with robustness validated across 40 trials under varying network topologies.
@inproceedings{chi2026vnc, title = {Fast-Adapting V2X Networks: A Dynamic Context Clustering Multi-Armed Bandits}, author = {Chi, Weiqi and He, Xiaoyang and Tsukada, Manabu}, booktitle = {IEEE Vehicular Networking Conference (VNC2026)}, year = {2026}, address = {Montreal, Canada} }
International Conf.
V2I Blockage Modeling and Performance Evaluation for Connected Autonomous Vehicle

Weiqi Chi, Jin Nakazato, Tomoki Murakami, and 1 more author

In IEEE Vehicular Technology Conference (VTC2024-Spring), 2024

Awarded Abs Bib PDF

Young Researcher’s Encouragement Award

The burgeoning Intelligent Transportation System (ITS) spurs global technological advancements, notably in innovative community development through vehicle-to-everything (V2X) communication. This study focuses on the high data rates and low latency offered by a millimeter-wave (mmWave) enabled vehicular network while addressing the significant challenge of link quality degradation due to blockages, exacerbated by the mmWave band’s small wavelength in high mobility and traffic conditions. We propose an RSU-assisted ITS system tailored for multi-lane, straight-road scenarios, effectively identifying blockage status for vehicles. Combining Simulation of Urban Mobility (SUMO) and MATLAB, this blockage-aware scheme lays the groundwork for future ITS enhancements. The research also delves into the effects of various frequency bands, vehicle types, and communication ranges, offering a holistic system performance analysis.
@inproceedings{chi2024v2i, title = {V2I Blockage Modeling and Performance Evaluation for Connected Autonomous Vehicle}, author = {Chi, Weiqi and Nakazato, Jin and Murakami, Tomoki and Tsukada, Manabu}, booktitle = {IEEE Vehicular Technology Conference (VTC2024-Spring)}, year = {2024}, }

news

Jun 11, 2026	🎤 Presented at IEEE VNC 2026 in Montreal!
May 20, 2026	🎙️ Student Volunteer at INFOCOM 2026!
Mar 13, 2026	🍊 My Ehime Internship is Now on YouTube!
Nov 23, 2025	🚀 1st TuPuHack, an AI Agent Adventure
Oct 20, 2025	🐈 My Personal Logo is Born!

latest posts

Jul 08, 2026	Part 8. Exposing Bit and Packet Error Rate
Jun 29, 2026	Part 7. Reporting Channel Quality Back
Jun 18, 2026	Part 6. Bootstrapping the World from OpenStreetMap