A Priori Based Deep Unfolding Method for mmWave Channel Estimation in MIMO Radar Aided V2X Communications

Due to the inherent high-mobility features in the Vehicles-to-Everything (V2X) scenarios, accurate channel estimation is essential to ensure the quality of communication services. Recently, multiple-input multiple-output (MIMO) radar has shown the potential to aid channel estimation. In this paper, we consider the MIMO radar aided V2X communication systems and propose a prior information aided deep learning method for channel estimation. Specifically, we use the MIMO radar to measure the angle information of moving vehicles. Based on the estimated angles, we obtain the non-zero position information of sparse angle-frequency channel. Then, by formulating the channel estimation as solving a group row sparse recovery problem, we propose a new shrinkage function and derive a priori assisted deep unfolding method. Experimental results show that the proposed method achieves the highest channel estimation accuracy compared with existing compressive sensing algorithms and deep-learning-based baseline methods.