Leveraging GRU Networks for Robust and Efficient Vehicular Channel Estimation

ABSTRACT

Accurate channel estimation is essential for robust vehicular communication, particularly under high‐mobility conditions where traditional methods like least squares, minimum mean square error, data pilot‐aided and orthogonal matching pursuit often fall short due to error propagation and computational inefficiencies. This paper presents an enhanced gated recurrent unit (GRU)‐based deep learning framework that incorporates attention‐based feature selection and adaptive noise filtering to improve channel estimation performance in IEEE 802.11p‐based vehicular networks. The proposed model effectively captures temporal and spatial dependencies in dynamic wireless environments without requiring prior channel knowledge. Simulation results show an average normalized mean squared error (NMSE) reduction of approximately 80.6% across diverse scenarios compared to conventional methods, demonstrating its superiority in both accuracy and computational efficiency. Notably, the model achieves approximately dB NMSE during epoch‐based convergence and maintains high‐resolution prediction quality, as validated through visual heatmaps and binned confusion‐matrix analysis. These findings position the proposed GRU‐based estimator as a scalable, real‐time solution for next‐generation vehicular communication systems.