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Review on CNN Algorithm based Radio Classification and RF Printing for Classifying Physical Layer based Radio Signals

Author(s):

Unnikrishnen Nampoothiry , RMDSSOE; Prof. V.M.Lomte, RMDSSOE

Keywords:

CNN, ML, Physical Layer, IQ Imbalance, K-nn, SVM, Oracle, RF, Radiofingerprinting, Shared Spectrum, MAC-ID, RSS, DC Offset, RELU, Deep Learning, USRP, MATLAB WLAN, Static Channel, Dynamic Channel, Confusion Matrix, EMD Matrix

Abstract

Every entity in this universe need to have a unique identification characteristic for it to associate with other components, in retrospect to this concept only the concept of Radio classification was carried out. The main prospect of this concept was the development of a unique way to give identification for radio signal using radio fingerprinting and identifying their uniqueness using CNN based and ML based algorithms. The key innovation here is to intentionally introduce controlled imperfections on the transmitter side through software directives, while minimizing the change in bit error rate. Unlike previous work that imposes constant environmental conditions, ORACLE adopts the ‘train once deploy anywhere’ paradigm with near perfect device classification accuracy (Kunal Sankhe, 2019).

Other Details

Paper ID: IJSRDV8I30613
Published in: Volume : 8, Issue : 3
Publication Date: 01/06/2020
Page(s): 575-578

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