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

arXiv: https://arxiv.org/abs/2403.08133 · HTML: https://mlsanity.com/paper/2403.08133/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 45 references that are in the map.
- First posted on arXiv 12 Mar 2024.

## Builds on (most cited first, 18 of 18)

- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [Deep Learning for Image Super-resolution: A Survey](https://mlsanity.com/paper/1902.06068/) (2019)
- [Pruning and Quantization for Deep Neural Network Acceleration: A Survey](https://mlsanity.com/paper/2101.09671/) (2021)
- [Implicit Diffusion Models for Continuous Super-Resolution](https://mlsanity.com/paper/2303.16491/) (2023)
- [Deep Learning-Based Channel Estimation](https://mlsanity.com/paper/1810.05893/) (2018)
- [Multi-resolution CSI Feedback with deep learning in Massive MIMO System](https://mlsanity.com/paper/1910.14322/) (2019)
- [Massive MIMO Channel Estimation with an Untrained Deep Neural Network](https://mlsanity.com/paper/1908.00144/) (2019)
- [CLNet: Complex Input Lightweight Neural Network designed for Massive MIMO CSI Feedback](https://mlsanity.com/paper/2102.07507/) (2021)
- [An Efficient Deep Learning Framework for Low Rate Massive MIMO CSI Reporting](https://mlsanity.com/paper/1912.10608/) (2019)
- [Deep Convolutional Compression for Massive MIMO CSI Feedback](https://mlsanity.com/paper/1907.02942/) (2019)
- [FDD Massive MIMO Uplink and Downlink Channel Reciprocity Properties: Full or Partial Reciprocity?](https://mlsanity.com/paper/1912.11221/) (2019)
- [CAnet: Uplink-aided Downlink Channel Acquisition in FDD Massive MIMO using Deep Learning](https://mlsanity.com/paper/2101.04377/) (2021)
- [Learning-Based Phase Compression and Quantization for Massive MIMO CSI Feedback with Magnitude-Aided Information](https://mlsanity.com/paper/2103.00432/) (2021)
- [A Markovian Model-Driven Deep Learning Framework for Massive MIMO CSI Feedback](https://mlsanity.com/paper/2009.09468/) (2020)
- [Deep Learning-based CSI Feedback for RIS-assisted Multi-user Systems](https://mlsanity.com/paper/2003.03303/) (2020)
- [CSI Feedback with Model-Driven Deep Learning of Massive MIMO Systems](https://mlsanity.com/paper/2112.06405/) (2021)
- [Deep Learning for Efficient CSI Feedback in Massive MIMO: Adapting to New Environments and Small Datasets](https://mlsanity.com/paper/2211.14785/) (2022)

## Abstract (arXiv)

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.