# Improving the Performance of R17 Type-II Codebook with Deep Learning

arXiv: https://arxiv.org/abs/2310.05962 · HTML: https://mlsanity.com/paper/2310.05962/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 23 references that are in the map.
- First posted on arXiv 13 Sep 2023.

## Builds on (most cited first, 9 of 9)

- [On the Spectral Bias of Neural Networks](https://mlsanity.com/paper/1806.08734/) (2018)
- [The Low-Rank Simplicity Bias in Deep Networks](https://mlsanity.com/paper/2103.10427/) (2021)
- [Overview of Deep Learning-based CSI Feedback in Massive MIMO Systems](https://mlsanity.com/paper/2206.14383/) (2022)
- [Lightweight Convolutional Neural Networks for CSI Feedback in Massive MIMO](https://mlsanity.com/paper/2005.00438/) (2020)
- [An Efficient Deep Learning Framework for Low Rate Massive MIMO CSI Reporting](https://mlsanity.com/paper/1912.10608/) (2019)
- [Deep Learning-based Implicit CSI Feedback in Massive MIMO](https://mlsanity.com/paper/2105.10100/) (2021)
- [Deep Learning Assisted Calibrated Beam Training for Millimeter-Wave Communication Systems](https://mlsanity.com/paper/2101.05206/) (2021)
- [PolarDenseNet: A Deep Learning Model for CSI Feedback in MIMO Systems](https://mlsanity.com/paper/2202.01246/) (2022)
- [Deep Learning Empowered Type-II Codebook: New Paradigm for Enhancing CSI Feedback](https://mlsanity.com/paper/2305.08081/) (2023)

## Built on by (most cited first, 1 of 1)

- [Neural Codebook Design for Network Beam Management](https://mlsanity.com/paper/2403.03053/) (2024)

## Abstract (arXiv)

The Type-II codebook in Release 17 (R17) exploits the angular-delay-domain partial reciprocity between uplink and downlink channels to select part of angular-delay-domain ports for measuring and feeding back the downlink channel state information (CSI), where the performance of existing deep learning enhanced CSI feedback methods is limited due to the deficiency of sparse structures. To address this issue, we propose two new perspectives of adopting deep learning to improve the R17 Type-II codebook. Firstly, considering the low signal-to-noise ratio of uplink channels, deep learning is utilized to accurately select the dominant angular-delay-domain ports, where the focal loss is harnessed to solve the class imbalance problem. Secondly, we propose to adopt deep learning to reconstruct the downlink CSI based on the feedback of the R17 Type-II codebook at the base station, where the information of sparse structures can be effectively leveraged. Besides, a weighted shortcut module is designed to facilitate the accurate reconstruction. Simulation results demonstrate that our proposed methods could improve the sum rate performance compared with its traditional R17 Type-II codebook and deep learning benchmarks.