loading page

An Investigation into the Impacts of Deep Learning-based Re-sampling on Specific Emitter Identification Performance
  • Mohamed Fadul,
  • Donald Reising,
  • Lakmali Weerasena
Mohamed Fadul
The University of Tennessee at Chattanooga

Corresponding Author:[email protected]

Author Profile
Donald Reising
The University of Tennessee at Chattanooga College of Engineering and Computer Science
Author Profile
Lakmali Weerasena
The University of Tennessee at Chattanooga
Author Profile


Increasing Internet of Things (IoT) deployments present a growing surface over which villainous actors can carry out attacks. This disturbing revelation is amplified by the fact that a majority of IoT devices use weak or no encryption at all. Specific Emitter Identification (SEI) is an approach intended to address this IoT security weakness. This work provides the first Deep Learning (DL) driven SEI approach that upsamples the signals after collection to improve performance while simultaneously reducing the hardware requirements of the IoT devices that collect them. DL-driven upsampling results in superior SEI performance versus two traditional upsampling approaches and a convolutional neural network only approach.
08 Nov 2022Submitted to The Journal of Engineering
09 Nov 2022Submission Checks Completed
09 Nov 2022Assigned to Editor
07 Aug 2023Reviewer(s) Assigned
14 Sep 2023Review(s) Completed, Editorial Evaluation Pending
17 Sep 2023Editorial Decision: Revise Major
16 Oct 20231st Revision Received
24 Oct 2023Submission Checks Completed
24 Oct 2023Assigned to Editor
24 Oct 2023Reviewer(s) Assigned
24 Oct 2023Review(s) Completed, Editorial Evaluation Pending
24 Oct 2023Editorial Decision: Accept