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A Model-Driven Channel Estimation Method for Millimeter-Wave Massive MIMO Systems.

Qingli Liu1, Yangyang Li1, Jiaxu Sun1

  • 1Communication and Network Laboratory, Dalian University, Dalian 116622, China.

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|March 11, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a new channel estimation method for millimeter-wave massive MIMO systems, improving accuracy by addressing beam squint. The model-driven approach enhances signal-to-noise ratio performance and speeds up convergence.

Keywords:
beam squintchannel estimationmassive MIMOmillimeter wavemodel driven

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Area of Science:

  • Electrical Engineering
  • Signal Processing
  • Wireless Communications

Background:

  • Millimeter-wave (mmWave) massive MIMO systems face challenges with low signal-to-noise ratio (SNR) channel estimation accuracy.
  • The "beam squint" effect, inherent in broadband mmWave systems, degrades estimation performance when not accounted for.

Purpose of the Study:

  • To propose a model-driven channel estimation method for mmWave massive MIMO broadband systems that accounts for the "beam squint" effect.
  • To enhance channel estimation accuracy and convergence speed, particularly under low SNR conditions.

Main Methods:

  • A model-driven approach integrating an iterative shrinkage threshold algorithm within a deep iterative network.
  • Transformation of the mmWave channel matrix into a sparse feature domain via learned training data.
  • Development of an attention-based contraction threshold network for beam domain denoising, adapting thresholds for varying SNRs.

Main Results:

  • Achieved an average increase of 17.28% in channel estimation accuracy across different SNRs.
  • Demonstrated a 10% increase in network convergence speed through joint optimization of residual and shrinkage threshold networks.
  • The proposed method effectively mitigates the "beam squint" effect, improving performance in low SNR scenarios.

Conclusions:

  • The proposed model-driven channel estimation method significantly enhances accuracy and convergence in mmWave massive MIMO broadband systems.
  • Accounting for the "beam squint" effect is crucial for reliable channel estimation in these systems.
  • The integration of deep learning techniques, specifically attention mechanisms and iterative shrinkage, offers a robust solution for mmWave signal processing challenges.