National Telecom Volunteer: Utilizing A Machine Learning Model To Predict Cellphone Network Coverage Using Big Data Analysis From Data Collected Through Crowdsourcing

In India remarkable developments have been taking place in the field of 5G technology and associated use cases after the launch of 5G in Oct 2022. However, no tool is available for real-time monitoring of the presence of telecom technology (2G/3G/4G/5G) and QoS (Quality of Service) within a region or to an individual. Thus, this paper proposes a methodology and related tools to measure ground-level QoS, results of which can be utilised by DoT (Department of Telecommunications), Government of India for taking proactive steps to provide high-quality telecom coverage to all citizens, focusing on strategic areas achieving the goals of the National Digital Communication Policy 2018. To achieve this, a smartphone app was developed to collect data from citizens who can share their locations and cell phone signal data as volunteers. Considering the country's population of almost ~1.4 billion, a 1% volunteer rate would generate significant data for the server. A highly available server architecture design has been proposed to handle this massive data. Mobile BTS (Base Transceiver Station), topographical and meteorological data from the government will be used along with collected signal data. Machine learning will forecast data for the missing locations since there will be few nationwide volunteers.