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Low-complexity iterative receiver based on variational Bayesian inference for multiple-input multiple-output

Wei-Zhe Li1,2, Xiao Han1,3, Yi-Zhen Jia1,2

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This study introduces a low-complexity receiver for underwater acoustic communication using variational Bayesian inference (VBI). The method enhances channel estimation and equalization, significantly reducing computational load for multiple-input multiple-output (MIMO) systems.

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Area of Science:

  • Underwater Acoustic Communication
  • Signal Processing
  • Information Theory

Background:

  • High-performance receivers are essential for underwater acoustic communication, particularly in multiple-input multiple-output (MIMO) systems.
  • Managing receiver complexity is a key challenge in these demanding environments.

Purpose of the Study:

  • To propose a low-complexity MIMO receiver for underwater acoustic communication.
  • To enhance channel estimation and equalization efficiency using variational Bayesian inference (VBI).

Main Methods:

  • Developed an iterative channel estimation model based on VBI, decomposing high-dimensional channels into sparse, low-dimensional vectors.
  • Integrated the Vector Approximate Message Passing (VAMP) technique into the VBI framework for channel estimation (TC-VAMP-VBI).
  • Proposed a serial iterative equalization algorithm using passive time reversal within the VBI framework.

Main Results:

  • The proposed algorithm significantly reduces computational complexity in MIMO systems.
  • Maintained robust channel estimation performance, even with short data blocks.
  • Validated through simulations and field experiment data.

Conclusions:

  • The VBI-based low-complexity MIMO receiver offers a practical solution for underwater acoustic communication.
  • The TC-VAMP-VBI channel estimation and passive time reversal equalization effectively reduce system complexity while ensuring performance.