A Comparison on Energy Detector and CNN-based Detector

In today's wireless communication technology, users are classified and assigned to the spectrum by licensing method. There are two types of users in cognitive radio technology, licensed and unlicensed. While primary users use a fixed frequency band, secondary users can detect frequency gaps by different methods, enabling communication when primary users are not using it. With cognitive radio technology, it is aimed to meet the increasing user demands and to make communication faster as a result of using spectrum gaps more efficiently. In this study, energy detector and convolutional neural network (CNN) are compared and investigated which can be more efficient in spectrum sensing.