Clustering-Enabled Tracking Areas Design for Beyond-5G Networks: A Live Network Demo

One of the current challenges still facing artificial intelligence (AI) and machine learning (ML) based solutions is their performance in real life environments. In this work, we report and analyze the outcomes of the deployment of one of our previously proposed AI-based automatic tracking areas (TA) design algorithm in a real and live mobile network. The implemented automation is based on our solution in [1], and has been experimented in an area with accidental land morphology that often leads to aggressive overshooting, and other radio issues affecting many high traffic sites. The area under consideration includes one major city, surrounding suburbs and villages, and main road traffic sections. We first analyze the characteristics of the considered area of deployment, collect the required statistics in terms of handover attempts, mobility event measurement reports (MRs), total paging requests per source site, and the intersite distances ISD. We then build a real dataset and construct the needed similarity matrix to run our ML-based algorithm. Finally, we present and compare the results collected after the deployment of our proposed solution in the live network with the initially existing TA plans. The obtained reduction in overhead costs of both tracking area update and paging signaling exceeded the expectations and the MNO targets