Comparison of techniques for radiometric identification based on deep convolutional neural networks. Issue 2 (1st January 2019)

• Record Type: Journal Article
• Authors: Baldini, G.; Gentile, C.; Giuliani, R.; Steri, G.
• Is Part Of: Electronics letters. Volume 55:Issue 2(2019)
• Page Start: 90
• Page End: 92
• Publication Date: 2019-01-01
• Subjects: time series -- image representation -- convolution -- neural nets -- wavelet transforms -- Gaussian noise; radiometric identification -- deep convolutional neural networks -- radio frequency emissions -- digitised RF -- 12 wireless devices -- deep CNN -- authors -- identification performance -- image representation -- RF‐to‐image techniques; Electronics -- Periodicals; 621.381
• DOI: 10.1049/el.2018.6229
• Languages: English
• ISSNs: 0013-5194

Abstract

Abstract : The authors investigate the application of deep convolutional neural networks (CNNs) to the problem of radiometric identification, i.e. the task of authenticating wireless devices on the basis of their radio frequency (RF) emissions, which contain features directly related to the physical properties of the wireless devices. They collected digitised RF from 12 wireless devices, and used various techniques to transform the time series derived from the RF to images. A deep CNN is then applied to the images. The authors' results show that the identification performance of the combination of deep CNN with an image representation significantly outperforms conventional methods based on dissimilarity on the original time series. Moreover, a specific comparison among RF‐to‐image techniques show that on their datasets the wavelet‐based approach outperforms other approaches, also in the presence of white Gaussian noise.