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<p>The increasing complexity of modern communication networks and medical imaging demands robust mathematical tools capable of handling both high-dimensional structures and uncertainty. This paper introduces a novel framework based on <i>complex fuzzy tensors</i> (CFTs), which integrate the descriptive power of tensor algebra with the uncertainty modeling ability of complex fuzzy sets. Building on this foundation, we develop a CFT–TOPSIS methodology for multicriteria decision-making that simultaneously incorporates magnitude and phase information, offering a richer and more reliable evaluation of alternatives. The proposed framework is applied to two representative case studies: beam selection in next-generation wireless MIMO systems and the assessment of medical image processing techniques. In both domains, the approach demonstrates superior performance in terms of robustness, interpretability, and accuracy when compared with traditional methods. The results confirm that CFT–TOPSIS not only provides theoretical advances in uncertainty modeling but also offers practical benefits for real-world decision problems where high-dimensionality and phase-sensitive information play a crucial role.</p>

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Complex Fuzzy Tensor-Based MCDM with TOPSIS for Wireless Beam Selection and Medical Imaging

  • 期刊论文

  • 发表日期：

    2026年7月1日

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  • Muhammad Bilal,
  • A. K. Alzahrani,
  • Ioan Lucian-Popa,
  • A. K. Aljahdali

摘要

The increasing complexity of modern communication networks and medical imaging demands robust mathematical tools capable of handling both high-dimensional structures and uncertainty. This paper introduces a novel framework based on complex fuzzy tensors (CFTs), which integrate the descriptive power of tensor algebra with the uncertainty modeling ability of complex fuzzy sets. Building on this foundation, we develop a CFT–TOPSIS methodology for multicriteria decision-making that simultaneously incorporates magnitude and phase information, offering a richer and more reliable evaluation of alternatives. The proposed framework is applied to two representative case studies: beam selection in next-generation wireless MIMO systems and the assessment of medical image processing techniques. In both domains, the approach demonstrates superior performance in terms of robustness, interpretability, and accuracy when compared with traditional methods. The results confirm that CFT–TOPSIS not only provides theoretical advances in uncertainty modeling but also offers practical benefits for real-world decision problems where high-dimensionality and phase-sensitive information play a crucial role.