DISSERTATION DEFENSE

Author : Jian Liu

Advisor: Dr. Chin-Tser Huang

Date: March 17, 2026

Time: 12:30 PM

Place: Online/Room 2267,  Storey Innovation Center

Abstract

In this dissertation, we address a key reliability challenge in connected vehicle networks: V2V links can degrade sharply under adverse weather, especially in 5G mmWave channels, where environmental attenuation can be severe in regions with dust and sandstorms. Because conducting controlled field experiments in extreme weather is costly and difficult, this dissertation develops simulation-driven solutions that characterize weather-induced degradation. First, it introduces the first open-source NS-3 weather simulator for studying the adverse weather impacts on 5G mmWave V2V communications, enabling systematic evaluation under diverse environmental conditions. Building on this capability, the dissertation investigates predictive analytics such as ARIMA, Prophet, LSTM, and GRU to forecast weather-related performance degradation. We use these predictions to design a proactive channel-switching strategy that transitions from 5G mmWave to 4G LTE before major reliability loss occurs. Next, it advances beyond prediction-based control by developing a deep reinforcement learning (DRL) channel-switching approach that learns optimal switching decisions online using cumulative throughput as feedback, enabling vehicles to adapt autonomously to real-time environmental changes. Finally, this dissertation proposes a weather-aware, reinforcement learning–based open-loop power control method for decentralized sidelink V2V communication. Each vehicle learns how to adjust its transmitted power using only information it can measure locally together with the extra path loss caused by weather. In simulations from clear weather to severe rain, this approach achieves higher packet reception ratio (PRR) than the baseline 3GPP strategy and existing open-loop power control methods.