Kalman-aided uplink channel estimation for time-varying multi-user vehicular STAR-RIS systems

Accurate uplink channel estimation in high-mobility vehicular networks remains challenging due to rapid channel variations and Doppler effects, especially in systems assisted by simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS). Most existing studies focus on limited user scenarios and static or low-mobility conditions, which restricts their applicability to realistic vehicular environments. This work proposes a Kalman-aided channel estimation framework for multi-user uplink vehicular STAR-RIS systems operating under high mobility. The proposed approach integrates least squares (LS) estimation with discrete Fourier transform (DFT)-based orthogonal pilot design to obtain initial channel estimates, followed by a Kalman filter to continuously track time-varying Rician fading channels modeled with Jakes temporal correlation. Path-loss scaling is incorporated to improve composite channel tracking accuracy. The framework effectively mitigates Doppler-induced estimation degradation and supports scalable multi-user operation. Simulation results show that the proposed estimator consistently outperforms conventional methods in high-mobility scenarios. In particular, the normalized mean square error is reduced by up to 25 dB for the time-switching protocol and approximately 22 dB for the energy-splitting protocol compared with the corresponding baseline estimation schemes. These results indicate that the proposed estimation and tracking framework maintains stable accuracy across a wide range of vehicular operating conditions.