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<p>In this paper, an online censoring diffusion augmented complex-valued normalized subband adaptive filtering (OCD-ACNSAF) algorithm is proposed for efficient processing of large-scale complex-valued data streams. The proposed algorithm integrates the online censoring (OC) strategy with the widely linear (WL) model to effectively handle both circular and non-circular complex-valued signals. A novel cost function is developed based on second-order statistical properties, and its minimization allows for accurate estimation while reducing processing and storage costs. The energy conservation framework is employed to analyze the algorithm’s performance. Extensive simulation results demonstrate that the proposed OCD-ACNSAF algorithm achieves lower mean square error and faster convergence compared to existing algorithms, while maintaining robustness for large-scale data streams.</p>

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Online censoring diffusion augmented complex-valued subband adaptive filtering

  • 期刊论文

  • 发表日期：

    2026年1月9日

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  • Shuyuan Zhang,
  • Pengwei Wen,
  • Zhao Li,
  • Caihong Gong,
  • Fangfang Bian,
  • Botao Jin

摘要

In this paper, an online censoring diffusion augmented complex-valued normalized subband adaptive filtering (OCD-ACNSAF) algorithm is proposed for efficient processing of large-scale complex-valued data streams. The proposed algorithm integrates the online censoring (OC) strategy with the widely linear (WL) model to effectively handle both circular and non-circular complex-valued signals. A novel cost function is developed based on second-order statistical properties, and its minimization allows for accurate estimation while reducing processing and storage costs. The energy conservation framework is employed to analyze the algorithm’s performance. Extensive simulation results demonstrate that the proposed OCD-ACNSAF algorithm achieves lower mean square error and faster convergence compared to existing algorithms, while maintaining robustness for large-scale data streams.