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Model-Data Hybrid-Driven Wideband Channel Estimation for Beamspace Massive MIMO Systems.

Yang Nie1,2, Zhenghuan Ma1, Lili Jing1,2

  • 1School of Physics and Electronic Information Engineering, Jining Normal University, Ulanqab 012000, China.

Entropy (Basel, Switzerland)
|February 27, 2026
PubMed
Summary

This study introduces a novel hybrid deep learning network for accurate beamspace channel estimation in massive MIMO systems. The proposed method enhances estimation accuracy and robustness, outperforming existing techniques in complex environments.

Keywords:
beamspace channel estimationmassive MIMOmillimeter-wavemodel-data

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Area of Science:

  • Electrical Engineering
  • Wireless Communications
  • Signal Processing

Background:

  • Accurate channel estimation is vital for beamforming and spectrally efficient transmission in beamspace massive MIMO systems.
  • Conventional methods struggle with model mismatches in complex propagation environments.
  • Deep learning approaches require large datasets and have limited generalization.

Purpose of the Study:

  • To develop a robust and accurate channel estimation scheme for wideband beamspace massive MIMO systems.
  • To overcome the limitations of traditional model-driven and data-driven methods.
  • To improve performance under various signal-to-noise ratios (SNRs).

Main Methods:

  • Proposed a model-data hybrid-driven network (MD-HDN) scheme.
  • Unfolded the Vector Approximate Message Passing (VAMP) algorithm into a trainable network.
  • Introduced a novel shrinkage function to enhance estimation accuracy.

Main Results:

  • The MD-HDN scheme significantly outperforms existing schemes across various SNRs.
  • Demonstrated substantial improvements in both estimation accuracy and robustness.
  • Validated through extensive numerical results.

Conclusions:

  • The proposed MD-HDN scheme offers a superior solution for wideband beamspace channel estimation.
  • The hybrid approach effectively combines model-based and data-driven techniques.
  • This method enhances the reliability of massive MIMO systems in real-world conditions.