# Spatio-Temporal Representation with Deep Neural Recurrent Network in MIMO CSI Feedback

arXiv: https://arxiv.org/abs/1908.07934 · HTML: https://mlsanity.com/paper/1908.07934/

## Key facts

- Cited by 4 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 4 Aug 2019.

## Built on by (most cited first, 4 of 4)

- [Overview of Deep Learning-based CSI Feedback in Massive MIMO Systems](https://mlsanity.com/paper/2206.14383/) (2022)
- [Deep Learning-based Implicit CSI Feedback in Massive MIMO](https://mlsanity.com/paper/2105.10100/) (2021)
- [Binarized Aggregated Network with Quantization: Flexible Deep Learning Deployment for CSI Feedback in Massive MIMO System](https://mlsanity.com/paper/2105.00354/) (2021)
- [Deep Learning for 1-Bit Compressed Sensing-based Superimposed CSI Feedback](https://mlsanity.com/paper/2203.06606/) (2022)

## Abstract (arXiv)

In multiple-input multiple-output (MIMO) systems, it is crucial of utilizing the available channel state information (CSI) at the transmitter for precoding to improve the performance of frequency division duplex (FDD) networks. One of the mainchallenges is to compress a large amount of CSI in CSI feedback transmission in massive MIMO systems. In this paper, we propose a deep learning (DL)-based approach that uses a deep recurrent neural network (RNN) to learn temporal correlation and adopts depthwise separable convolution to shrink the model. The feature extraction module is also elaborately devised by studyingdecoupled spatio-temporal feature representations in different structures. Experimental results demonstrate that the proposed approach outperforms existing DL-based methods in terms of recovery quality and accuracy, which can also achieve remarkable robustness at low compression ratio (CR).