Yang, J., Ai, B., Chen, W., Yang, S., Wang, N., & Yuen, C. (2026). Deep Unfolding-Based Sensing-Assisted Channel Estimation With Imperfect Radar Arrays. IEEE Journal on Selected Areas in Communications, 44, 401–415. https://doi.org/10.1109/jsac.2025.3610425
Abstract:
In vehicle-to-everything (V2X) scenarios, the high dynamic characteristics of V2X environments impose significant challenges on communication channel estimation, where the emerging integrated sensing and communication technology could serve as a vital tool for achieving accurate channel estimation. This paper leverages radar-sensed angle information to assist in communication channel estimation and proposes a deep unfolding-based radar-assisted channel estimation network (Radar-CEnet). Specifically, for the radar module, to address the challenges posed by insufficient data in imperfect arrays, we employ a model-agnostic meta-learning with a convolutional neural network (MAML-CNN) approach to achieve high-precision direction-of-arrival (DOA) estimation. Then, the angle information obtained by the radar module, as prior knowledge, is used for channel estimation. Building on this, we design a novel soft-thresholding shrinkage function and propose the Radar-CEnet algorithm to efficiently estimate the sparse channel. Finally, we rigorously prove the convergence of the Radar-CEnet algorithm and demonstrate that it achieves a lower estimation error. Experimental results show that the proposed Radar-CEnet outperforms existing traditional methods and deep learning-based approaches in channel estimation performance. At an SNR of 20dB, the proposed Radar-CEnet method reduces the NMSE from –23.75 dB to –27.15 dB compared to the learning-based iterative soft-thresholding method, achieving an estimation accuracy improvement of approximately 54%.
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Funding Info:
This research / project is supported by the MOE and MOF, China - Fundamental Research Funds for the Central Universities
Grant Reference no. : 2022JBQY004
This research / project is supported by the Natural Science Foundation of China - NA
Grant Reference no. : 62221001, U2468201, and W2421083
This research / project is supported by the State Scholarship Fund, China Scholarship Council - NA
Grant Reference no. : 202307090109
This research / project is supported by the Agency for Science, Technology and Research Singapore - Manufacturing, Trade, and Connectivity Programmatic
Grant Reference no. : M22L1b0110
This research / project is supported by the Ministry of Education Singapore - Academic Research Fund Tier 2
Grant Reference no. : T2EP50124-0032
This research / project is supported by the National Research Foundation - Future Communications Research and Development Programme (FCP)
Grant Reference no. : FCP-NTURG- 2024-025