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Sparse direct adaptive equalization based on proportionate recursive least squares algorithm for multiple-input

Zhen Qin1, Jun Tao2, Xiao Han1

  • 1Acoustic Science and Technology Laboratory, Harbin Engineering University, Harbin, 150001, China.

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Summary
This summary is machine-generated.

Sparse adaptive equalization using proportionate recursive least squares (PRLS) algorithms improves multiple-input multiple-output (MIMO) underwater acoustic (UWA) communications. The proportionate stable fast transversal filters (PSFTF) direct adaptive equalizers demonstrated superior performance in real-world trials.

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Area of Science:

  • Signal Processing
  • Underwater Communications
  • Adaptive Filtering

Background:

  • Underwater acoustic (UWA) communication systems face challenges due to channel sparsity and multipath.
  • Adaptive equalization is crucial for mitigating distortion in UWA channels.
  • Existing algorithms may not fully exploit sparsity for improved performance.

Purpose of the Study:

  • To investigate sparse direct adaptive equalization for MIMO UWA communications using the proportionate recursive least squares (PRLS) algorithm.
  • To evaluate the performance of a fast implementation of PRLS, the proportionate stable fast transversal filters (PSFTF), in direct adaptive equalizers.
  • To compare PSFTF-based equalizers with selective zero-attracting stable fast transversal filter equalizers (SZA-SFTF-DAEs).

Main Methods:

  • Performance analysis of the PRLS algorithm for sparse systems.
  • Implementation of a direct adaptive decision-feedback equalizer using the PSFTF algorithm.
  • Comparison of PSFTF direct adaptive equalizers (DAEs) with SZA-SFTF-DAEs.
  • Experimental validation using an at-sea MIMO UWA communication trial.

Main Results:

  • PRLS algorithm shows performance gains over standard recursive least squares in sparse systems.
  • PSFTF direct adaptive decision-feedback equalizers outperform PSFTF direct adaptive linear equalizers.
  • Experimental results indicate that PSFTF-DAEs outperform SZA-SFTF-DAEs in real-world MIMO UWA communication trials.

Conclusions:

  • Sparse direct adaptive equalization based on PRLS algorithms is effective for MIMO UWA communications.
  • PSFTF-based direct adaptive equalizers offer a promising approach for enhancing UWA communication performance.
  • The proportionate updating principle in PSFTF algorithms leads to superior performance compared to zero-attracting sparsity-promoting principles in this context.