Department of Electrical Engineering, Faculty of Engineering, Center of Excellence in Artificial Intelligence, Machine Learning, and Smart Grid Technology, Chulalongkorn University, Bangkok, Thailand
Ngoc Thien Le
Department of Electrical Engineering, Faculty of Engineering, Center of Excellence in Artificial Intelligence, Machine Learning, and Smart Grid Technology, Chulalongkorn University, Bangkok, Thailand
Department of Electrical Engineering, Faculty of Engineering, Center of Excellence in Artificial Intelligence, Machine Learning, and Smart Grid Technology, Chulalongkorn University, Bangkok, Thailand
Wattanasak Srisiri
Department of Electrical Engineering, Faculty of Engineering, Center of Excellence in Artificial Intelligence, Machine Learning, and Smart Grid Technology, Chulalongkorn University, Bangkok, Thailand
Department of Electrical Engineering, Faculty of Engineering, Center of Excellence in Artificial Intelligence, Machine Learning, and Smart Grid Technology, Chulalongkorn University, Bangkok, Thailand
Department of Electrical Engineering, Faculty of Engineering, Center of Excellence in Artificial Intelligence, Machine Learning, and Smart Grid Technology, Chulalongkorn University, Bangkok, Thailand
Department of Electrical Engineering, Faculty of Engineering, Center of Excellence in Artificial Intelligence, Machine Learning, and Smart Grid Technology, Chulalongkorn University, Bangkok, Thailand
In order to support the development of more efficient spectrum management by using Big Data and Artificial Intelligence (AI), the authors study and propose a methodological framework that allows the application of Big Data and AI into spectrum management. The authors benchmark how spectrum regulators across the world are currently applying Big Data and AI technologies into their spectrum management, together with advantage(s) and disadvantage(s) of each of the approaches. The authors analyze the current status of the spectrum management under Thailand’s Office of the National Broadcasting and Telecommunications Commission (NBTC). Moreover, the authors identify gaps that might exist between the current status and the aimed future in which Big Data and AI technologies could be applied, and how to close the gaps so that the more efficient spectrum management could be achieved. Based on these studies and analyses, the authors propose a framework and a prototype of a web application applying Big Data and AI Platform to support the mission on spectrum management of the Office of the NBTC, Thailand.
