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This study introduces low-complexity sparse recursive least squares (RLS) algorithms for underwater acoustic (UWA) communications. These novel algorithms effectively improve sparse direct adaptive equalizer (DAE) performance in UWA channels.

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Area of Science:

  • Underwater Acoustic Communications
  • Signal Processing
  • Adaptive Filtering

Background:

  • Sparse direct adaptive equalizers (DAEs) show promise for underwater acoustic (UWA) communications.
  • Conventional sparse DAEs offer performance benefits over non-sparse versions.
  • Recursive least squares (RLS) sparse DAEs are understudied due to high complexity.

Purpose of the Study:

  • To develop low-complexity sparse RLS algorithms for multiple-input multiple-output (MIMO) UWA channel equalization.
  • To address the complexity challenges associated with RLS-based sparse DAEs.
  • To enhance the performance of sparse DAEs in UWA environments.

Main Methods:

  • Development of novel low-complexity sparse RLS algorithms.
  • Application to multiple-input multiple-output (MIMO) UWA channel equalization.
  • Experimental validation of algorithm effectiveness.

Main Results:

  • The proposed fast sparse RLS DAEs demonstrate effectiveness in UWA channel equalization.
  • Experimental results confirm superior performance compared to non-sparse counterparts.
  • Achieved significant complexity reduction for RLS-based sparse DAEs.

Conclusions:

  • Low-complexity sparse RLS algorithms are viable for MIMO UWA communications.
  • The developed algorithms offer a practical solution for improving UWA communication performance.
  • This work paves the way for more efficient sparse DAEs in challenging UWA environments.