Channel Estimation and Resource Allocation for RIS-Assisted Vehicular Networks: A Full-Duplex P-NOMA Approach

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.