Spectrum Allocation in 5G and Beyond Intelligent Ubiquitous Networks

ABSTRACT Effective spectrum allocation in 5G and beyond intelligent ubiquitous networks is vital for predicting future frequency band needs and ensuring optimal network performance. As wireless communication evolves from 4G to 5G and beyond, it has brought about remarkable advancements in speed and connectivity. However, with the growing demand for higher data rates and increased network capacity, new challenges in managing and utilizing network frequencies have emerged. Accurately forecasting spectrum requirements is critical to addressing these challenges. This research explores how machine learning (ML) plays a pivotal role in optimizing network performance through intelligent decision‐making, predictive analysis, and adaptive management of network resources. By leveraging ML algorithms, networks can autonomously self‐optimize in real time, adjusting to changing conditions and improving performance in 5G and beyond. The effectiveness of our approach was demonstrated through an extensive case study, which showed that it not only meets spectrum requirements in various environments but also significantly reduces energy consumption by pinpointing the appropriate spectrum range for each location. These results underscore the approach's potential for enhancing spectrum management in future networks, offering a scalable and efficient solution to the challenges facing 5G and beyond. Effective spectrum allocation in 5G and beyond networks is crucial for meeting future frequency demands and optimizing network performance. This research demonstrates how machine learning (ML) can enhance spectrum management by enabling real‐time, adaptive decision‐making. By analyzing network data and predicting resource needs, our approach autonomously optimizes network performance, improving efficiency and reducing energy consumption. A case study showed that the ML‐driven model not only meets spectrum demands across various environments but also significantly enhances energy efficiency, offering a scalable solution for future network challenges. Here is the graphical representing effective spectrum allocation in 5G and beyond intelligent networks using ML. The image captures key elements like network towers, dynamic data flows, and ML algorithms adjusting network parameters in real time. It also illustrates improved speed, connectivity, spectrum efficiency, and energy consumption reduction.