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Kalman-aided uplink channel estimation for time-varying multi-user vehicular STAR-RIS systems
Aswiniya Ambigapathy1, Sriharipriya Kc2
1School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
Scientific Reports
|June 22, 2026
Summary
This study introduces a Kalman-aided framework for accurate channel estimation in high-mobility vehicular networks using simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS). The method significantly improves estimation accuracy and stability in challenging mobile conditions.
Area of Science:
- Wireless Communications
- Signal Processing
- Intelligent Transportation Systems
Background:
- Accurate channel estimation is crucial for vehicular networks, particularly with reconfigurable intelligent surfaces (RIS).
- High mobility and Doppler effects in vehicular environments pose significant challenges to existing estimation techniques.
- Current methods often lack applicability in realistic, high-speed, multi-user scenarios.
Purpose of the Study:
- To develop a robust channel estimation framework for multi-user uplink vehicular systems employing STAR-RIS under high mobility.
- To address the limitations of existing methods in dynamic and fast-changing vehicular channel conditions.
- To enhance the performance and scalability of channel estimation in advanced wireless networks.
Main Methods:
- A Kalman-aided channel estimation framework integrating Least Squares (LS) estimation and Discrete Fourier Transform (DFT)-based orthogonal pilot design.
- Utilizing a Kalman filter for continuous tracking of time-varying Rician fading channels with Jakes temporal correlation.
- Incorporating path-loss scaling to refine composite channel tracking accuracy.
Main Results:
- The proposed framework effectively mitigates Doppler-induced estimation degradation in high-mobility vehicular STAR-RIS systems.
- Achieved significant reductions in normalized mean square error (up to 25 dB) compared to baseline schemes.
- Demonstrated stable and accurate performance across diverse vehicular operating conditions, supporting scalable multi-user operation.
Conclusions:
- The Kalman-aided framework provides a substantial advancement in channel estimation for high-mobility vehicular networks with STAR-RIS.
- The proposed method offers superior accuracy and robustness over conventional techniques in dynamic environments.
- This work enables more reliable communication and efficient resource utilization in future intelligent transportation systems.
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