Enhancing Cooperative Spectrum Sensing in Flying Cell Towers for Disaster Management Using Convolutional Neural Networks

Abstract

Natural calamities are increasing every year and communication plays a major role in post disaster measures to save human lives. This work utilizes the adaptation of the emerging dynamic radio technology called cognitive radio networks over Unmanned Aerial vehicles (UAV). Enhancing emergency communication over disaster affected zones where the mobile network base stations are completely destroyed is enabled by mounting drones with an omni antenna base station. This chapter analyses the cooperative spectrum sensing (CSS) technique of the intelligent radio to study incoming primary user (PU) when the available spectrum consists of multiple secondary users (SUs). A deep learning based technique called SpecCNN (Spectrum sensing Convolutional Neural Network) is proposed for performing intelligent spectrum sensing by analysing hidden cyclostationary features from drone data (image) of disastrous areas.