# Aggregated Network for Massive MIMO CSI Feedback

arXiv: https://arxiv.org/abs/2101.06618 · HTML: https://mlsanity.com/paper/2101.06618/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 23 references that are in the map.
- First posted on arXiv 17 Jan 2021.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Convolutional Neural Network based Multiple-Rate Compressive Sensing for Massive MIMO CSI Feedback: Design, Simulation, and Analysis](https://mlsanity.com/paper/1906.06007/) (2019)
- [Multi-resolution CSI Feedback with deep learning in Massive MIMO System](https://mlsanity.com/paper/1910.14322/) (2019)
- [Deep Learning for Joint Channel Estimation and Feedback in Massive MIMO Systems](https://mlsanity.com/paper/2011.07242/) (2020)
- [Deep Learning based Downlink Channel Prediction for FDD Massive MIMO System](https://mlsanity.com/paper/1908.03360/) (2019)
- [Deep Learning based Denoise Network for CSI Feedback in FDD Massive MIMO Systems](https://mlsanity.com/paper/2004.07576/) (2020)
- [Compression and Acceleration of Neural Networks for Communications](https://mlsanity.com/paper/1907.13269/) (2019)
- [Deep Convolutional Compression for Massive MIMO CSI Feedback](https://mlsanity.com/paper/1907.02942/) (2019)
- [Bit-level Optimized Neural Network for Multi-antenna Channel Quantization](https://mlsanity.com/paper/1909.10730/) (2019)
- [Adversarial Attack on DL-based Massive MIMO CSI Feedback](https://mlsanity.com/paper/2002.09896/) (2020)
- [Binary Neural Network Aided CSI Feedback in Massive MIMO System](https://mlsanity.com/paper/2011.02692/) (2020)
- [AnciNet: An Efficient Deep Learning Approach for Feedback Compression of Estimated CSI in Massive MIMO Systems](https://mlsanity.com/paper/2008.07112/) (2020)

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

- [PolarDenseNet: A Deep Learning Model for CSI Feedback in MIMO Systems](https://mlsanity.com/paper/2202.01246/) (2022)

## Abstract (arXiv)

In frequency division duplexing (FDD) mode, it is necessary to send the channel state information (CSI) from user equipment to base station. The downlink CSI is essential for the massive multiple-input multiple-output (MIMO) system to acquire the potential gain. Recently, deep learning is widely adopted to massive MIMO CSI feedback task and proved to be effective compared with traditional compressed sensing methods. In this paper, a novel network named ACRNet is designed to boost the feedback performance with network aggregation and parametric RuLU activation. Moreover, valid approach to expand the network architecture in exchange of better performance is first discussed in CSI feedback task. Experiments show that ACRNet outperforms loads of previous state-of-the-art feedback networks without any extra information.