mlsanityData to 14 Oct 2026

Binarized Aggregated Network with Quantization: Flexible Deep Learning Deployment for CSI Feedback in Massive MIMO System

arXiv 2105.00354 ↗First posted 1 May 2021cs.IT cs.AI eess.SPcited by 5

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Abstract (arXiv)

Massive multiple-input multiple-output (MIMO) is one of the key techniques to achieve better spectrum and energy efficiency in 5G system. The channel state information (CSI) needs to be fed back from the user equipment to the base station in frequency division duplexing (FDD) mode. However, the overhead of the direct feedback is unacceptable due to the large antenna array in massive MIMO system. Recently, deep learning is widely adopted to the compressed CSI feedback task and proved to be effective. In this paper, a novel network named aggregated channel reconstruction network (ACRNet) is designed to boost the feedback performance with network aggregation and parametric rectified linear unit (PReLU) activation. The practical deployment of the feedback network in the communication system is also considered. Specifically, the elastic feedback scheme is proposed to flexibly adapt the network to meet different resource limitations. Besides, the network binarization technique is combined with the feature quantization for lightweight and practical deployment. Experiments show that the proposed ACRNet outperforms loads of previous state-of-the-art networks, providing a neat feedback solution with high performance, low cost and impressive flexibility.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 28 of its 44 references that are in the map. First posted on arXiv 1 May 2021.

Builds on

28 of 44 references.

References · 28

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  11. 2020Deep Learning for Distributed Channel Feedback and Multiuser Precoding in FDD Massive MIMOcited by 26
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16 more
  1. 2013Five Disruptive Technology Directions for 5Gcited by 15
  2. 2020Lightweight Convolutional Neural Networks for CSI Feedback in Massive MIMOcited by 14
  3. 2020Deep Learning for Joint Channel Estimation and Feedback in Massive MIMO Systemscited by 14
  4. 2019Deep Learning based Downlink Channel Prediction for FDD Massive MIMO Systemcited by 13
  5. 2020Deep Learning based Denoise Network for CSI Feedback in FDD Massive MIMO Systemscited by 13
  6. 2019Compression and Acceleration of Neural Networks for Communicationscited by 11
  7. 2019Deep Convolutional Compression for Massive MIMO CSI Feedbackcited by 9
  8. 2019Bit-level Optimized Neural Network for Multi-antenna Channel Quantizationcited by 9
  9. 2020Adversarial Attack on DL-based Massive MIMO CSI Feedbackcited by 9
  10. 2020Binary Neural Network Aided CSI Feedback in Massive MIMO Systemcited by 9
  11. 2020AnciNet: An Efficient Deep Learning Approach for Feedback Compression of Estimated CSI in Massive MIMO Systemscited by 7
  12. 2021CAnet: Uplink-aided Downlink Channel Acquisition in FDD Massive MIMO using Deep Learningcited by 6
  13. 2019Spatio-Temporal Representation with Deep Neural Recurrent Network in MIMO CSI Feedbackcited by 4
  14. 2020A Markovian Model-Driven Deep Learning Framework for Massive MIMO CSI Feedbackcited by 4
  15. 2020Deep Learning for Massive MIMO Channel State Acquisition and Feedbackcited by 3
  16. 2020A Deep Learning Framework for Hybrid Beamforming Without Instantaneous CSI Feedbackcited by 3

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Cited by year

Citing papers per year: 2022: 3; 2023: 1; 2024: 0; 2025: 1.

5 papers.

  1. 2022Overview of Deep Learning-based CSI Feedback in Massive MIMO Systemscited by 37
  2. 2022Learning Representations for CSI Adaptive Quantization and Feedbackcited by 3
  3. 2022Quantization Adaptor for Bit-Level Deep Learning-Based Massive MIMO CSI Feedbackcited by 3
  4. 2023Dig-CSI: A Distributed and Generative Model Assisted CSI Feedback Training Frameworkcited by 1
  5. 2025Information-Preserving CSI Feedback: Invertible Networks with Endogenous Quantization and Channel Error Mitigationcited by 0

Similar, not cited

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  1. 2022Changeable Rate and Novel Quantization for CSI Feedback Based on Deep Learning
  2. 2022A Learnable Optimization and Regularization Approach to Massive MIMO CSI Feedback
  3. 2024Zone-Specific CSI Feedback for Massive MIMO: A Situation-Aware Deep Learning Approach
  4. 2023Accelerating and Compressing Deep Neural Networks for Massive MIMO CSI Feedback
  5. 2022Machine Learning-Based CSI Feedback With Variable Length in FDD Massive MIMO
  6. 2024Vector Quantization for Deep-Learning-Based CSI Feedback in Massive MIMO Systems
  7. 2024An Efficient Network with Novel Quantization Designed for Massive MIMO CSI Feedback
  8. 2022Design of an Efficient CSI Feedback Mechanism in Massive MIMO Systems: A Machine Learning Approach using Empirical Data