mlsanityData to 14 Oct 2026

Aggregated Network for Massive MIMO CSI Feedback

arXiv 2101.06618 ↗First posted 17 Jan 2021cs.IT cs.AI eess.SPcited by 1

Not on the map. Why?

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.

Key facts. Cited by 1 paper 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

14 of 23 references.

References · 14

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,611
  3. 2016Aggregated Residual Transformations for Deep Neural Networkscited by 2,546
  4. 2019Convolutional Neural Network based Multiple-Rate Compressive Sensing for Massive MIMO CSI Feedback: Design, Simulation, and Analysiscited by 35
  5. 2019Multi-resolution CSI Feedback with deep learning in Massive MIMO Systemcited by 32
  6. 2020Deep Learning for Joint Channel Estimation and Feedback in Massive MIMO Systemscited by 14
  7. 2019Deep Learning based Downlink Channel Prediction for FDD Massive MIMO Systemcited by 13
  8. 2020Deep Learning based Denoise Network for CSI Feedback in FDD Massive MIMO Systemscited by 13
  9. 2019Compression and Acceleration of Neural Networks for Communicationscited by 11
  10. 2019Deep Convolutional Compression for Massive MIMO CSI Feedbackcited by 9
  11. 2019Bit-level Optimized Neural Network for Multi-antenna Channel Quantizationcited by 9
  12. 2020Adversarial Attack on DL-based Massive MIMO CSI Feedbackcited by 9
2 more
  1. 2020Binary Neural Network Aided CSI Feedback in Massive MIMO Systemcited by 9
  2. 2020AnciNet: An Efficient Deep Learning Approach for Feedback Compression of Estimated CSI in Massive MIMO Systemscited by 7

Built on by

Cited by year

Citing papers per year: 2022: 1.

1 paper.

  1. 2022PolarDenseNet: A Deep Learning Model for CSI Feedback in MIMO Systemscited by 2