Near-Field Channel Estimation and Sparse Reconstruction for FDD XL-MIMO Systems

IF 3.7 3区计算机科学 Q2 TELECOMMUNICATIONS

IEEE Communications Letters Pub Date : 2025-02-14 DOI:10.1109/LCOMM.2025.3542482

Ze Wang;Guoping Zhang;Ji Wang;Hongbo Xu

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引用次数: 0

Abstract

The exponential growth of antennas in extremely large-scale MIMO (XL-MIMO) systems can lead to substantial overhead in pilot transmission for channel estimation and feedback, resulting in a decline in spectrum efficiency. This letter proposes a deep learning (DL)-based framework tailored for frequency division duplex (FDD) XL-MIMO, focusing on specialized neural networks for channel estimation and sparse reconstruction. For channel estimation, we design frequency-aware pilots by using dense layers according to the signal model and develop an attention mechanism-based residual channel estimation (A-RCE) network, which leverages inherent correlations within the channel matrix across subcarriers and antennas to improve estimation accuracy. To reduce channel state information (CSI) feedback overhead, we introduce a trainable fast iterative shrinkage thresholding (TFIST) network that leverages the polar-domain sparsity of the near-field channel to achieve a low-dimensional sparse representation. The simulation results validate the effectiveness of our proposed scheme, which can significantly enhance the estimation performance compared to other benchmark schemes.

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来源期刊

IEEE Communications Letters 工程技术-电信学

CiteScore

8.10

自引率

7.30%

发文量

590

审稿时长

2.8 months

期刊介绍： The IEEE Communications Letters publishes short papers in a rapid publication cycle on advances in the state-of-the-art of communication over different media and channels including wire, underground, waveguide, optical fiber, and storage channels. Both theoretical contributions (including new techniques, concepts, and analyses) and practical contributions (including system experiments and prototypes, and new applications) are encouraged. This journal focuses on the physical layer and the link layer of communication systems.