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

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.