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<p>Ensuring reliability and fault tolerance in distributed systems while processing massive datasets presents a significant challenge, particularly when dealing with nonlinear signal relationships. To address this issue, we propose a novel distributed bilinear model and develop a diffusion augmented Volterra bilinear adaptive filtering algorithm (D-AVBAF) for complex-valued signals. Unlike conventional linear algorithms that offer limited problem-solving capabilities, our methodology synergistically integrates linear and nonlinear components to effectively identify hidden patterns in complex data structures. Theoretical analysis proves the stability of the proposed model. Furthermore, simulation comparisons show that the proposed D-AVBAF algorithm outperforms others in different noise environments, demonstrating its effectiveness and competitiveness in practical applications.</p>

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Diffusion Augmented Bilinear Adaptive Filtering

  • 期刊论文

  • 发表日期：

    2026年1月19日

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  • Jiayin Wang,
  • Dailin Song,
  • Yunhe Guan,
  • Guobing Qian

摘要

Ensuring reliability and fault tolerance in distributed systems while processing massive datasets presents a significant challenge, particularly when dealing with nonlinear signal relationships. To address this issue, we propose a novel distributed bilinear model and develop a diffusion augmented Volterra bilinear adaptive filtering algorithm (D-AVBAF) for complex-valued signals. Unlike conventional linear algorithms that offer limited problem-solving capabilities, our methodology synergistically integrates linear and nonlinear components to effectively identify hidden patterns in complex data structures. Theoretical analysis proves the stability of the proposed model. Furthermore, simulation comparisons show that the proposed D-AVBAF algorithm outperforms others in different noise environments, demonstrating its effectiveness and competitiveness in practical applications.