Recent Research

• AI enabled Vehicular communication

• Multi-Objective Reinforcement Learning

• Quantum Computing

• Autonomous Driving

• Aerial/Terrestrial Vehicular Networks

• Large Language Model

• Diffusion Model

• Semantic Communication

RF-Thz-HighwayEnv

A Novel simulation testbed that emulates multi-band wireless network-enabled VNet RF-THz-Highway-Env based on HighwayEnv. This test environment not only inherits the advantages of autonomous driving, and lane changes on the highway, but also implements RF/THz channel propagation modeling, network selection, and HO control. [read more].

Hybrid LLM-DDQN Optimization

Large language models (LLMs) have received considerable interest recently due to their outstanding reasoning and comprehension capabilities.This work explores applying LLMs to vehicular networks, aiming to jointly optimize vehicle-to-infrastructure (V2I) communications and autonomous driving (AD) policies. [read more].

UAVs Path Planning and Aerial Communication

Simultaneously optimize the multi-UAV cell-association decisions and their moving speed decisions on a given 3D aerial highway. The objective is to improve both the transportation and communication performances, e.g., collisions, connectivity, and HOs. [read more].

Quantum Machine Learning

We explore the integration of variational quantum circuits (VQCs) and Condition Value at Risk (CVaR) to optimize the kinematics and network connectivity of autonomous vehicles (AVs) under stochastic conditions, such as wireless channels and traffic dynamics. [read more].