Radar-Assisted Compressed Sensing for Enhanced Channel Estimation in V2X Communications

Channel estimation is a fundamental component in modern vehicular-to-everything (V2X) communication systems, supporting efficient and reliable data transmission in dynamic and often resource-constrained environments. Traditional methods of channel estimation are resource-intensive, relying on significant amounts of training data and computational power, which limits their applicability in real-time V2X scenarios. Compressed Sensing (CS) has emerged as a promising solution to alleviate these demands by reducing both training data requirements and computational complexity. However, in dense urban environments characterized by prevalent non-line-of-sight (NLOS) conditions, traditional CS algorithms are challenged by complex multipath effects and signal degradation. To overcome these challenges, we propose a novel radar-assisted CS framework that leverages radar sensing to enhance the adaptability and accuracy of channel estimation under strong NLOS conditions. Our simulation results validate the efficacy of this approach, demonstrating marked improvements in both estimation accuracy and reliability across challenging V2X communication scenarios.