Editorial: Machine learning-based spectrum occupancy prediction and resource allocation/management for wireless communication systems

Editorial on the Research Topic Machine learning-based spectrum occupancy prediction and resource allocation/management for wireless communication systemsOver the past 2 decades, wireless communication systems have undergone a rapid transformation, marked by the transition from fourth generation (4G) to fifth generation (5G) and the ongoing development of sixth generation (6G).This evolution has been driven by the exponential growth of connected devices, the proliferation of the Internet of Things (IoT), and the increasing demand for high data rates, ultra-reliable low-latency communications, and massive machine-type connectivity.These trends inevitably exert unprecedented pressure on spectrum resources, which are inherently scarce and limited.Hence, efficient spectrum management has become one of the most pressing challenges in the design of next-generation wireless networks.Conventional static allocation strategies are no longer adequate to cope with the highly dynamic nature of modern wireless environments.The coexistence of heterogeneous services, fluctuating traffic patterns, and diverse quality-of-service requirements necessitates more flexible and intelligent approaches to spectrum occupancy prediction (SOP) and resource allocation.In this context, machine learning (ML) has emerged as a transformative tool.By leveraging vast amounts of data and advanced learning architectures, ML can capture hidden patterns in spectrum usage, predict occupancy trends across multiple domains, and enable adaptive allocation decisions in real time.This paradigm shift aligns closely with the vision of cognitive and self-organizing networks, where intelligence is embedded into every layer of the system to enhance efficiency, adaptability, and resilience.Against this backdrop, the Guest Editors proposed this Research Topic to collect original and high-quality contributions addressing the opportunities and challenges of ML-based SOP and resource allocation/management.The aim is to explore both theoretical