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Area of Science:

  • Signal Processing
  • Underwater Communications

Background:

  • Channel equalization is vital for single-carrier underwater acoustic (UWA) communications.
  • Existing vector approximate message passing frequency-domain turbo equalization (VAMP-FDTE) requires pre-determined noise power, which is challenging in dynamic UWA environments.

Purpose of the Study:

  • To develop an enhanced VAMP-FDTE scheme that learns noise power online.
  • To improve the robustness and performance of UWA communication systems.

Main Methods:

  • Proposed an enhanced VAMP-FDTE scheme incorporating the expectation-maximization (EM) algorithm for online noise power estimation.
  • Utilized intermediate VAMP-FDTE results for EM-based noise power learning with minimal extra computational overhead.

Main Results:

  • The enhanced VAMP-FDTE, named EM-VAMP-FDTE, demonstrated superior performance compared to the standard VAMP-FDTE.
  • Experimental data from shallow-sea horizontal UWA communication trials with MIMO configuration validated the improved performance.

Conclusions:

  • Online noise power learning via the EM algorithm significantly enhances VAMP-FDTE performance in UWA communications.
  • The EM-VAMP-FDTE scheme offers a practical solution for UWA systems facing unknown and dynamic noise conditions.