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REVIEW article

Front. Comms. Net.
Sec. Wireless Communications
Volume 6 - 2025 | doi: 10.3389/frcmn.2025.1482698
This article is part of the Research Topic Machine Learning-Based Spectrum Occupancy Prediction and Resource Allocation/Management for Wireless Communication Systems View all articles

Machine Learning-Based Spectrum Occupancy Prediction: A Comprehensive Survey

Provisionally accepted
Mehmet Ali Aygul Mehmet Ali Aygul 1*Hakan Ali Cirpan Hakan Ali Cirpan 1Huseyin Arslan Huseyin Arslan 2
  • 1 Istanbul Technical University, Istanbul, Türkiye
  • 2 Istanbul Medipol University, Istanbul, Türkiye

The final, formatted version of the article will be published soon.

    In cognitive radio (CR) systems, efficient spectrum utilization depends on the ability to predict spectrum opportunities. Traditional statistical methods for spectrum occupancy prediction (SOP) are insufficient for addressing the non-stationary nature of spectrum occupancy, especially with UEs' increased mobility and diversity in the sixth-generation and beyond wireless networks.This survey provides a comprehensive overview of machine learning (ML)-based SOP methods that address these challenges. The paper begins with a brief discussion of problem definition and traditional statistical methods before delving into a detailed survey of ML-based methods.Various aspects of SOP are analyzed from a CR perspective, highlighting the multidimensional correlations in spectrum usage across time, frequency, space, etc. Key challenges and enabling methods for effective prediction are reviewed, focusing on deep learning methods that exploit these multidimensional correlations. The survey also covers dataset generation techniques for SOP. Additionally, the paper discusses CR threats that impair spectrum utilization and reviews ML methods for detecting these threats. The future directions for ML-based SOP are also given.

    Keywords: 6G, Cognitive Radio, deep learning, machine learning, Multi-dimensions, spectrum occupancy prediction

    Received: 18 Aug 2024; Accepted: 06 Jan 2025.

    Copyright: © 2025 Aygul, Cirpan and Arslan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Mehmet Ali Aygul, Istanbul Technical University, Istanbul, Türkiye

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.