Blind Identification of Radio Access Techniques Based on Time-Frequency Analysis and Convolutional Neural Network
Shrishail M. Hiremath, Siddharth Deshmukh, R Rakesh, Sarat Kumar Patra · 2018
In the Last decade, various machine learning schemes have been investigated to make the cognitive radio (CR) more adaptive. Blind identification of radio accesses technology (RAT) indirectly aid the CR to adapt according to the real-time wireless environment. In this paper, some of the various wireless standards like GSM, Bluetooth and Wi-Fi are blindly identified using deep neural networks. The present work proposes the combination of time-frequency distributions and Convolutional Neural Network (CNN) based Machine Learning technique to identify the RATs. Time-Frequency Analysis (TFA) is used to obtain the spectral content of the signal and Convolutional Neural Network is used for feature extraction and identification purpose. The accuracy of the network is analyzed with performance plots of correct identification and the confusion matrix. Also, Performance of the deep neural network classifier has been compared with the previously proposed Machine Learning techniques.