Physics-Inspired Deep Learning Anti-Aliasing Framework in Efficient Channel State Feedback
arxiv(2024)
摘要
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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