Physics-Inspired Deep Learning Anti-Aliasing Framework in Efficient Channel State Feedback

• URL: http://arxiv.org/abs/2403.08133v1
• Date: Tue, 12 Mar 2024 23:40:51 GMT
• Title: Physics-Inspired Deep Learning Anti-Aliasing Framework in Efficient Channel State Feedback
• Authors: Yu-Chien Lin, Yan Xin, Ta-Sung Lee, Charlie (Jianzhong) Zhang, and Zhi Ding
• Abstract summary: This work introduces a new CSI upsampling framework at the gNB as a post-processing solution to address the gaps caused by undersampling. We also develop a learning-based method that integrates the proposed algorithm with the Iterative Shrinkage-Thresholding Algorithm Net (ISTA-Net) architecture. Our numerical results show that both our rule-based and deep learning methods significantly outperform traditional techniques and current state-of-the-art approaches in terms of performance.
• Score: 25.68689988641748
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Acquiring downlink channel state information (CSI) at the base station is vital for optimizing performance in massive Multiple input multiple output (MIMO) Frequency-Division Duplexing (FDD) systems. While deep learning architectures have been successful in facilitating UE-side CSI feedback and gNB-side recovery, the undersampling issue prior to CSI feedback is often overlooked. This issue, which arises from low density pilot placement in current standards, results in significant aliasing effects in outdoor channels and consequently limits CSI recovery performance. To this end, this work introduces a new CSI upsampling framework at the gNB as a post-processing solution to address the gaps caused by undersampling. Leveraging the physical principles of discrete Fourier transform shifting theorem and multipath reciprocity, our framework effectively uses uplink CSI to mitigate aliasing effects. We further develop a learning-based method that integrates the proposed algorithm with the Iterative Shrinkage-Thresholding Algorithm Net (ISTA-Net) architecture, enhancing our approach for non-uniform sampling recovery. Our numerical results show that both our rule-based and deep learning methods significantly outperform traditional interpolation techniques and current state-of-the-art approaches in terms of performance.

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