Machine learning-based spectrum occupancy prediction: a comprehensive survey

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.