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Successive weight update sparse Bayesian learning based receiver for multiuser underwater acoustic communications.

Xueli Sheng1,2,3, Zheng Wu1,2,3, Li Wei1,2,3

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This study introduces a new multiuser receiver for underwater acoustic communications, improving performance in challenging conditions. The novel approach enhances channel estimation accuracy and significantly reduces computational complexity.

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Area of Science:

  • Underwater Acoustic Communications
  • Signal Processing
  • Wireless Communication Systems

Background:

  • Underwater acoustic communication systems face significant challenges from time-varying channels and multiple-access interference.
  • Existing multiuser receivers struggle to maintain performance under these adverse conditions.

Purpose of the Study:

  • To propose a novel single-carrier multiuser receiver for underwater acoustic communications.
  • To enhance channel estimation accuracy and reduce computational complexity in challenging acoustic environments.

Main Methods:

  • Integration of soft successive interference cancellation with successive weight update sparse Bayesian learning channel estimation based on approximate message passing (SWUSBL-AMP).
  • Implementation of an improved channel update decision mechanism to address time-varying channel mismatches.
  • Serial processing of factor graphs within SWUSBL-AMP to accelerate convergence and improve accuracy.

Main Results:

  • The proposed receiver demonstrates superior bit error rate performance compared to existing receivers using experimental data from the South China Sea.
  • The SWUSBL-AMP algorithm achieves nearly an order of magnitude lower computational complexity than comparable algorithms.

Conclusions:

  • The developed multiuser receiver effectively mitigates interference and improves channel estimation in time-varying underwater acoustic environments.
  • The proposed SWUSBL-AMP algorithm offers a computationally efficient and accurate solution for underwater acoustic communication systems.