Cutting-Edge Technology: An Intelligent Channel Allocation and Spectrum Sensing Procedures Over 5G Heterogeneous Networks

The Spectrum Insight Predictive System (SIPS) represents a groundbreaking approach to optimizing spectrum allocation and enhancing network performance in 5G heterogeneous networks. With the exponential increase in data traffic and the advent of
$5\mathrm{G}$
technologies, managing spectrum resources efficiently has become imperative. SIPS addresses this challenge by integrating advanced machine learning algorithms, including K-means clustering and ARIMA forecasting, to intelligently predict spectrum demand and dynamically allocate channels, thereby ensuring efficient spectrum utilization. This paper presents a comprehensive evaluation of SIPS, showcasing its significant impact on network operations and user experience. Through meticulous analysis, SIPS identifies common traffic patterns and peak usage times, leveraging these insights to predict future spectrum demands with high precision. The system then utilizes decision tree algorithms to dynamically allocate spectrum resources, adapting in real-time to the evolving needs of the network. Performance metrics, both before and after SIPS implementation, demonstrate substantial improvements in average throughput, packet loss rates, and spectrum utilization across various times of the day, indicating a more efficient and reliable network.