RF Fingerprint Classification With Combinatorial-Randomness-Based Power Amplifiers and Convolutional Neural Networks: Secure analog/RF electronics and electromagnetics-Reference-Cited by-同舟云学术

RF Fingerprint Classification With Combinatorial-Randomness-Based Power Amplifiers and Convolutional Neural Networks: Secure analog/RF electronics and electromagnetics

Published:2022 Issue:4 Volume:14 Page:28-36
ISSN:1943-0582
Container-title:IEEE Solid-State Circuits Magazine
language:
Short-container-title:IEEE Solid-State Circuits Mag.

Author:

Chen Vanessa¹^ORCID,Xu Jiachen²^ORCID,Shen Yuyi¹^ORCID,Chen Ethan³^ORCID

Affiliation:

1. Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, USA

2. Ph.D. degree, Carnegie Mellon University, Pittsburgh, USA

3. Energy-Efficient Circuits and Systems Lab, Carnegie Mellon University, Pittsburgh, USA

Funder

National Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering

Link

https://ieeexplore.ieee.org/ielam/4563670/9950726/9950754-aam.pdf

Reference15 articles.

1. Analysis of impersonation attacks on systems using RF fingerprinting and low-end receivers

2. Injecting Reliable Radio Frequency Fingerprints Using Metasurface for the Internet of Things

3. Visualizing Time Series Data with Temporal Matching Based t-SNE

4. Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference

5. Bayesian Neural Networks for Identification and Classification of Radio Frequency Transmitters Using Power Amplifiers’ Nonlinearity Signatures

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. PUF-Assisted Radio Frequency Fingerprinting Exploiting Power Amplifier Active Load-Pulling;IEEE Transactions on Information Forensics and Security;2024