Spectrum Utilization Analytics and Forecasting for Open RAN Deployment

Abstract

Radio frequency spectrum is an invaluable resource that calls for smart management solutions with the advent of the fifth generation wireless networks and the use of the Open RAN approach. Spectrum use analytics is the use of advanced data science methods for the interpretation of real-time data and historical data extracted from distributed radios on the network. Autoregressive integrated models and deep learning models provide the means for the prediction of capacity needs and congestion maps along the timeline and geography. Synergistic methods with RAN Intelligent Controller provide the means for closed-loop automation and the use of smart applications for automatic allocation according to demands for the use of the spectrum resource. The use of reinforcement learning provides models for the autonomous allocation process along the timeline with the interpretation of the environment and the use of rewards for the learning process. Implementation methods in this context involve the use of computing infrastructure and the integration with the existing legacy systems for the management of networks with testing for the continuity of the service. The use of smart spectrum management solutions provides benefits such as increased efficiency in the spectrum resource use, reduced latency in the networks, and increased quality of experience.