Primary User Spectrum Prediction Based on Supervised Model using Deep Radio

Abstract

Sensing time plays an important role in Cognitive radio functionalities. The main approach of this paper is to predict the pattern of the primary user spectrum in order to reduce the battery power of the cognitive user in sensing the primary user spectrum continuously. We have developed a CNN-LSTN based model for predicting a traffic method with more accuracy. This method is relatively tested for different frequency bands of existing mobile operators using the deep radio.