Channel state information (CSI) reporting is important for multiple-input multiple-output (MIMO) wireless transceivers to achieve high capacity and energy efficiency in frequency division duplex (FDD) mode. CSI reporting for massive MIMO systems could consume large bandwidth and degrade spectrum efficiency. Deep learning (DL)-based CSI reporting integrated with channel characteristics has demonstrated success in improving CSI compression and recovery. To further improve the encoding efficiency of CSI feedback, we develop an efficient DL-based compression framework CQNet to jointly tackle CSI compression, codeword quantization, and recovery under the bandwidth constraint. CQNet is directly compatible with other DL-based CSI feedback works for further enhancement. We propose a more efficient quantization scheme in the radial coordinate by introducing a novel magnitude-adaptive phase quantization framework. Compared with traditional CSI reporting, CQNet demonstrates superior CSI feedback efficiency and better CSI reconstruction accuracy.
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This content will become publicly available on August 2, 2025
Physics-Inspired Deep Learning Anti-Aliasing Framework in Efficient Channel State Feedback
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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- PAR ID:
- 10529679
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE transactions on wireless communications
- ISSN:
- 1536-1276
- Page Range / eLocation ID:
- Accepted
- Subject(s) / Keyword(s):
- Deep unfolding, CSI upsampling, massive MIMO, CSI recovery.
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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