Thesis Format
Monograph
Degree
Master of Science
Program
Electrical and Computer Engineering
Supervisor
Fang, Fang
2nd Supervisor
Wang, Xianbin
Co-Supervisor
Abstract
Ensuring consistent and reliable communication in vehicular networks is challenging due to the high mobility of vehicles. To address this issue, this study presents a solution involving spectrum reuse, effective interference management, and adaptive channel estimation tailored for dynamic vehicular environments. This study proposes an innovative communication model featuring full-duplex transmission and non-orthogonal multiple access (NOMA) that enhances spectrum efficiency and effectively tackles interference, a major issue in highly mobile settings. Specially, optimizes spectrum reuse between base station (BS)-reconfigurable intelligent surface (RIS)-vehicle, and vehicle-to-vehicle (V2V) communications, improving vehicular connectivity with limited spectral resources. Incorporating RIS mitigates signal degradation and enhances connectivity by dynamically reconfiguring the wireless propagation environment. Moreover, an imperfect channel is also regarded as a more realistic scenario. To tackle the complexities of channel estimation in RIS-assisted vehicular networks, our research designs a two-phased channel estimation methodology, which significantly reduces overhead while ensuring accurate channel estimation. In the initial phase, a duplex pilot transmission, which involves simultaneous transmission and reception of pilot signals to efficiently estimate the channel, is employed to estimate the stable segment of the channel between RIS and BS. Building upon the estimated RIS-BS channel, the subsequent phase employs a crafted pilot signal to estimate the cascade channel. Furthermore, the thesis delves into optimal power allocation and optimizes RIS phase shifts to achieve power consumption minimization and data rate maximization considering quality of service (QoS) constraints. The comprehensive simulation results demonstrate that the proposed approach outperforms existing techniques.
Summary for Lay Audience
In our increasingly connected world, cars that communicate with each other and with traffic networks could transform driving, making it safer and more efficient. Imagine vehicles sharing critical information—like alerting each other to sudden road hazards or traffic jams—especially when moving at high speeds. But enabling these smooth, reliable connections is no easy task. Constant movement and crowded urban environments make it challenging for vehicles to maintain strong, clear signals, which are essential for both safety and convenience.
Our study explores a new solution to keep these connections strong and efficient, even under tough conditions. We combine several advanced technologies to help vehicles share data effectively. First, we use a technique called “non-orthogonal multiple access” (NOMA), which allows multiple vehicles to use the same communication channel simultaneously. Think of it as letting cars share a lane without slowing each other down, maximizing the flow of information without needing extra bandwidth. Alongside NOMA, we use “full-duplex transmission,” which enables cars to send and receive data at the same time, further improving the communication flow.
To boost signal reliability, we introduce “reconfigurable intelligent surfaces” (RIS). These smart surfaces act like flexible mirrors, reflecting signals to keep them strong even when buildings or other cars block the way. RIS helps cars maintain a stable connection in busy cityscapes where obstacles often disrupt signals. Additionally, our system is designed to adapt to imperfect signals, a common real-world issue due to fast-moving vehicles and changing environments.
To ensure that our approach works seamlessly, we created a two-step method to quickly and accurately estimate the quality of each connection. This helps our system manage data flow better, reducing slowdowns that could interfere with communication. In our tests, this framework outperformed traditional methods by providing faster, more reliable data rates. With this design, we’re paving the way for safer, more connected driving in the smart cities of tomorrow, where cars can “talk” to each other as they navigate busy roads.
Recommended Citation
Mokhtari, Somayeh, "Channel Estimation and Resource Allocation for RIS-Assisted Vehicular Networks: A Full-Duplex P-NOMA Approach" (2025). Electronic Thesis and Dissertation Repository. 10700.
https://ir.lib.uwo.ca/etd/10700
