Sensing-Aided Channel Estimation in OFDM Systems by Leveraging Communication Echoes

Integrated sensing and communication (ISAC) is a promising technique that simultaneously provides communication and sensing services, thereby attracting significant attention as a hot topic for next wireless systems. Leveraging communication signal echoes for environmental sensing holds significant potential. Channel estimation (CE), being closely related to environmental information, stands to benefit from incorporating this echo sensing. However, despite the promise of ISAC, the utilization of communication signal echo sensing in sensing-assisted CE has not been well explored, hampering the progress in enhancing CE accuracy. To bridge this gap and overcome this challenge, we propose a sensing-aided CE method tailored specifically for vehicle-to-everything (V2X) communication scenarios using orthogonal frequency-division multiplexing (OFDM) modulation, extracting sensing prior information from communication echoes. The proposed scheme employs radar signal processing algorithms to extract sensing parameters from received communication echoes at the base station (BS). Subsequently, the false path suppression is performed to refine these sensing parameters, yielding echo sensing-based prior information reflecting the channel attributes between the target vehicle and the BS. By leveraging this prior information, an echo sensing-aided CE is proposed to fully exploit channel sparsity and bounded characteristics in the delay-Doppler (DD) domain, thereby enhancing CE accuracy. Simulation results demonstrate the effectiveness of the proposed method in rectifying estimation error by using echo sensing information. Notably, the proposed method not only achieves significantly improved CE accuracy compared to classic enhancement methods and deep learning (DL) methods, but also exhibits robustness against the parameter variations.