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Deep Learning-Based Joint CSI Feedback and Hybrid Precoding in FDD mmWave Massive MIMO Systems.

Qiang Sun1,2, Huan Zhao1, Jue Wang1

  • 1School of Information Science and Technology, Nantong University, Nantong 226019, China.

Entropy (Basel, Switzerland)
|April 23, 2022
PubMed
Summary

This study introduces an end-to-end deep learning method for channel state information (CSI) feedback and hybrid precoding in millimeter wave systems. The approach bypasses channel reconstruction, improving performance with limited feedback resources.

Keywords:
CSI feedbackdeep learninghybrid precodingmassive MIMOmillimeter wave

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Area of Science:

  • Wireless Communications
  • Signal Processing
  • Machine Learning

Background:

  • Millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems offer high bandwidth but require efficient channel state information (CSI) feedback and precoding.
  • Traditional methods often involve separate CSI reconstruction and hybrid precoding, which can be suboptimal and resource-intensive.

Purpose of the Study:

  • To propose an integrated, end-to-end deep learning framework for CSI feedback and hybrid precoding in frequency division duplexing (FDD) mmWave MIMO systems.
  • To develop a novel neural network that directly designs hybrid precoders and combiners from compressed CSI feedback, eliminating the need for explicit channel reconstruction.

Main Methods:

  • An end-to-end deep learning network was designed, combining CSI feedback and hybrid precoding functionalities.
  • The network learns to generate hybrid precoders and combiners directly from feedback codewords, which are compressed representations of CSI.
  • The proposed method bypasses the conventional channel reconstruction phase.

Main Results:

  • The proposed end-to-end deep learning approach demonstrated superior performance compared to conventional hybrid precoding schemes.
  • Performance gains were particularly significant under limited feedback resource conditions.
  • The integrated network effectively handled both CSI feedback compression and hybrid precoding optimization.

Conclusions:

  • An end-to-end deep learning framework offers a more efficient and effective solution for CSI feedback and hybrid precoding in FDD mmWave MIMO systems.
  • Eliminating the separate channel reconstruction step leads to performance improvements, especially when communication resources are constrained.
  • This approach represents a promising direction for optimizing mmWave MIMO system performance through integrated deep learning.