Yang, J., Ai, B., Chen, W., Yang, S., Shi, G., Wang, N., & Yuen, C. (2025). Deep Learning-Based Near-Field Wideband Channel Estimation: A Joint LISTA-CP Approach. IEEE Transactions on Vehicular Technology, 74(9), 14041–14053. https://doi.org/10.1109/tvt.2025.3561798
Abstract:
Extremely large-scale multiple-input-multiple-output (XL-MIMO) is listed as one of the key 6G candidate technologies due to its ability to exponentially increase the communication spectrum efficiency. However, wideband XL-MIMO channel estimation faces a new challenge known as near-field beam split effect. To cope with this challenge, we propose a deep learning based joint learned iterative shrinkage thresholding algorithm with partial weight-coupling (Joint LISTA-CP) to accurately recover the channel. Specifically, by studying the characteristics of the near-field wideband channel, we employ a frequency-dependent polar domain dictionary to ensure that the sparse polar domain channel vectors across different subcarriers share a common sparse support. Then, we transform the near-field wideband channel estimation problem into a multiple measurement vector recovery problem. Utilizing the common sparsity structure of different subcarriers, we design the Joint LISTA-CP network that integrates multiple sub-networks. The proposed network employs a multidimensional shrinkage thresholding operator to guarantee the common sparse property of different subcarrier channels. In addition, we rigorously demonstrate that the Joint LISTA-CP network achieves lower recovery error. Simulation results show that the Joint LISTA-CP method enhances the estimation accuracy of the near-field wideband channels.
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Funding Info:
This research is supported by core funding from: 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. : U2468201, W2421083, 62221001
This research is supported by core funding from: The State Scholarship Fund, China Scholarship Council
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-NTU-RG-2024-025