Intelligent Crowd-Sourced 5G Heat-map with Event-driven Architecture

Abstract

5G networks are expected to revolutionize the mobile network and IoT industries. Increased data transfer speeds, reduced latency, and extended bandwidths enable the true power of 5G networks. To support increased bandwidths cellular industry looked to high-frequency bands with high data rates in the spectrum above 24 GHz which are typically called “millimeter waves”. The introduction of millimeter waves reduces the coverage radius drastically due to high penetration losses and the blocking nature of this wave spectrum. The reduced coverage radius causes higher infrastructure costs for the network providers. This research will focus on a crowdsourcing mechanism where network data is collected through a mobile application and use this data to generate a real-time network coverage map. In addition, collected data will be used to predict future network quality demands and locations for cell towers with the help of machine learning technologies. The outcome of this research will be beneficial to network providers to reduce infrastructure costs by optimally laying infrastructures.