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A Correntropy-Based Proportionate Affine Projection Algorithm for Estimating Sparse Channels with Impulsive Noise.

Zhengxiong Jiang1, Yingsong Li1,2, Xinqi Huang1

  • 1College of Information and Communications Engineering, Harbin Engineering University, Harbin 150001, China.

Entropy (Basel, Switzerland)
|December 3, 2020
PubMed
Summary
This summary is machine-generated.

A new proportionate affine projection maximum correntropy criterion (PAPMCC) algorithm improves sparse channel estimation. This robust method excels in impulsive noise, outperforming traditional algorithms for network echo and wireless communications.

Keywords:
impulsive noise environmentsmaximum correntropy criterionproportionate affine projection algorithmsparse channel estimation

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Area of Science:

  • Signal Processing
  • Communications Engineering
  • Adaptive Filtering

Background:

  • Sparse channel estimation is crucial for network echo and wireless communications.
  • Traditional proportionate affine projection (AP) algorithms suffer performance degradation in impulsive noise.
  • Impulsive noise environments pose significant challenges for accurate channel identification.

Purpose of the Study:

  • To develop a novel robust proportionate affine projection (AP) algorithm for sparse channel estimation.
  • To enhance the performance of AP algorithms in impulsive noise environments.
  • To introduce the proportionate affine projection maximum correntropy criterion (PAPMCC) algorithm.

Main Methods:

  • The proposed algorithm integrates the maximum correntropy criterion (MCC) with the data reusing scheme of AP algorithms.
  • The PAPMCC algorithm is derived within a channel estimation framework.
  • Performance is evaluated through extensive simulations using various input signals.

Main Results:

  • The PAPMCC algorithm demonstrates superior performance compared to existing AP algorithms under impulsive noise.
  • The proposed method effectively overcomes the identification performance degradation caused by impulsive noise.
  • Simulations confirm the robustness and accuracy of the PAPMCC algorithm.

Conclusions:

  • The novel PAPMCC algorithm offers a robust solution for sparse channel estimation in impulsive noise.
  • This algorithm provides significant improvements over traditional AP methods in challenging noise conditions.
  • The PAPMCC algorithm is a promising advancement for applications like network echo cancellation and wireless communication systems.