SBIR Phase I: 5G Network Performance and Demand Prediction for Smart Cities

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The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will result from enabling self-evolving telecommunication networks to ensure that reliable and high-speed internet access is available in large metropolitan areas and mission critical applications in smart cities will receive guaranteed Quality of Service, while the capital and operational costs of the networks are reduced. Furthermore, this technology will reduce the energy consumption of telecommunication networks by moving resources where and when they are needed, avoiding over-provisioning and waste in idle resources.This Small Business Innovation Research (SBIR) Phase I project develops a first of its kind novel Artificial Intelligent-based network performance and demand prediction platform to guarantee 5G connectivity in metropolitan areas and eventually in smart cities. With 68% of the world population living in urban areas by 2050, constant movement of people and diversity in 5G applications network requirements, network optimization is critical. However, achieving optimal network configuration requires accurate prediction of future network demand. The proposed research will utilize the external contextual data of mass human movement and their activity along with a portfolio of machine learning methodologies to perform accurate network demand prediction and consequently optimal resource allocation. The main objective of this project is to develop and deploy an automated cloud-based software that performs prediction on network key performance indicators in urban areas up to seven days in advance. This software solution enables network operators to identify and anticipate accurate temporal and spatial demands and anomalies, understand the factors that will cause demand variations, and pinpoint future opportunities for optimization based on this information.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.