Power, Control, and Data Processing Systems

Power, Control, and Data Processing Systems

ML-and VIKOR for Anomaly Detection and Cell Ranking in 5G/B5G

Document Type : Original Research

Authors
1 Telecommunications Infrastructure Company Tehran, Iran
2 Departman Electrical and Computer Eng. of Semnan University Verified email at semnan.ac.ir
3 Department of Electrical and Computer Engineering, Semnan, Iran
4 Associate Professor of Communications/IEEE Senior Member
10.30511/pcdp.2025.2072728.1047
Abstract
This study introduces a hybrid framework that integrates supervised machine learning (ML) algorithms with the VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) multi-criteria decision-making (MCDM) technique to advance anomaly detection and cell ranking in next-generation networks. The proposed model addresses critical challenges in heterogeneous network environments, including data imbalance and fault prioritization. Three ML algorithms—Naïve Bayes, Decision Tree, and Random Forest—were evaluated, with Random Forest achieving the highest accuracy (93.658%). However, the Decision Tree algorithm demonstrated optimal efficiency, balancing high accuracy (92.688%) with the fastest execution time (0.04 seconds), rendering it particularly suitable for real-time applications. The incorporation of VIKOR enhanced the framework by enabling fault prioritization based on severity and impact, improving detection of minority fault classes, and supporting multi-criteria resource management. This hybrid approach resulted in improved system accuracy, flexibility, and scalability, ultimately contributing to reduced operational response times and enhanced network reliability. The findings validate the efficacy of combining ML with MCDM for intelligent fault management and cell ranking in complex network ecosystems
Keywords

Subjects


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Volume 3, Issue 1
Winter 2026
Pages 38-47

  • Receive Date 25 September 2025
  • Revise Date 26 November 2025
  • Accept Date 28 November 2025
  • First Publish Date 28 November 2025
  • Publish Date 01 March 2026