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  • 1College of Electronic Information Engineering, Inner Mongolia University, Hohhot 010021, China.

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Summary
This summary is machine-generated.

This study introduces a new algorithm to improve direction-of-arrival (DOA) estimation in massive MIMO systems, overcoming challenges from antenna mutual coupling and off-grid errors for better channel state information (CSI) acquisition.

Keywords:
DOA estimationarray mutual couplingmassive MIMOsparse bayesian learning (SBL)

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Area of Science:

  • Wireless communication systems
  • Signal processing
  • Array signal processing

Background:

  • Accurate direction-of-arrival (DOA) estimation is vital for channel state information (CSI) acquisition in massive multiple-input multiple-output (MIMO) systems.
  • Existing DOA algorithms suffer performance degradation due to antenna mutual coupling and off-grid errors in practical scenarios.

Purpose of the Study:

  • To develop a robust DOA estimation algorithm that mitigates the adverse effects of mutual coupling and off-grid errors.
  • To enhance the accuracy and reliability of CSI acquisition in massive MIMO systems.

Main Methods:

  • Modeling the array output signal vector using mutual coupling coefficients to transform the DOA problem into block sparse signal reconstruction and parameter optimization.
  • Proposing a novel sparse Bayesian learning (SBL)-based algorithm utilizing the expectation-maximization (EM) algorithm for iterative parameter estimation.
  • Employing polynomial roots for grid refinement and dynamic grid point updates to address off-grid errors and improve DOA accuracy.

Main Results:

  • The proposed SBL-based algorithm demonstrates enhanced robustness against mutual coupling and off-grid errors.
  • Simulation results confirm superior DOA estimation performance compared to existing algorithms.
  • Improved accuracy in parameter estimation leads to more reliable CSI acquisition.

Conclusions:

  • The novel SBL algorithm effectively addresses mutual coupling and off-grid errors in DOA estimation for massive MIMO systems.
  • The proposed method offers a significant improvement in estimation performance and robustness, crucial for practical wireless communication applications.