TOWARDS COGNITIVE AUTONOMOUS NETWORKS IN 5G

Cell densification and addition of new Radio Access Technologies have been the solutions of choice for improving area-spectral efficiency to serve the ever-growing traffic demand. Both solutions, however, increase the cost and complexity of network operations for which the agreed solution is increased automation. Cognitive Autonomous Networks (CAN) will therefore use Artificial Intelligence and Machine Learning (ML) to maximize the value of automation. This paper develops the models,for cognitive automation and proposes a CAN design that addresses the requirements,for 5G and future networks. We then illustrate the benefit of this approach by evaluating ML models that learn a network's response to different mobility states and configurations.