Transmitter Classification with Supervised Deep Learning

Morin, Cyrille; Cardoso, Leonardo S.; Hoydis, Jakob; Gorce, Jean‐Marie; Vial, Thibaud

doi:10.1007/978-3-030-25748-4_6

Cited by 37 publications

(9 citation statements)

References 11 publications

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“…Second, default keys or credentials can be brute-forced by high-computing-power attackers, extracted from device firmware or mobile apps, or intercepted at login [2]. Third, the ID number such as MAC address in the header needs extra spectral or power resources, which is limited in IoT applications, to be transmitted [3]. Hence, a passive authentication mechanism without cryptographic materials or IDs may be the future of IoT identity authentication.…”

Section: Introductionmentioning

confidence: 99%

Radio Frequency Fingerprint Identification Based on Denoising Autoencoders

Zhou

et al. 2019

2019 International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)

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Radio Frequency Fingerprinting (RFF) is one of the promising passive authentication approaches for improving the security of the Internet of Things (IoT). However, with the proliferation of low-power IoT devices, it becomes imperative to improve the identification accuracy at low SNR scenarios. To address this problem, this paper proposes a general Denoising Au-toEncoder (DAE)-based model for deep learning RFF techniques. Besides, a partially stacking method is designed to appropriately combine the semi-steady and steady-state RFFs of ZigBee devices. The proposed Partially Stacking-based Convolutional DAE (PSC-DAE) aims at reconstructing a high-SNR signal as well as device identification. Experimental results demonstrate that compared to Convolutional Neural Network (CNN), PSCDAE can improve the identification accuracy by 14% to 23.5% at low SNRs (from -10 dB to 5 dB) under Additive White Gaussian Noise (AWGN) corrupted channels. Even at SNR = 10 dB, the identification accuracy is as high as 97.5%.

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Section: Introductionmentioning

confidence: 99%

Radio Frequency Fingerprint Identification Based on Denoising Autoencoders

Zhou

et al. 2019

2019 International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)

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“…Deep learning techniques have enjoyed significant success in solving challenging problems in several application domains, including image classification, medical bio-informatics, natural language translation amongst others. Motivated by these developments, researchers in wireless communication have embraced deep learning to address the most difficult problems in wireless communications and networking, such as interference identification, classification [34], [101] and suppression [92], modulation recognition [112], transmitter classification [113], spectrum sensing [114], traffic classification [115] amongst other challenges. In addition, deep learning techniques can be very useful in managing coexistence issues arising from the deployment of fifth generation (5G) New Radio (NR) and sixth generation (6G) technologies with regards to scalability, generalization and the ability to learn from the data rather than reliance on domain expertise.…”

Section: Deep Learning Fundamentalsmentioning

confidence: 99%

Interference Suppression Using Deep Learning: Current Approaches and Open Challenges

2022

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In light of the finite nature of the wireless spectrum and the increasing demand for spectrum use arising from recent technological breakthroughs in wireless communication, the problem of interference continues to persist. Despite recent advancements in resolving interference issues, interference still presents a difficult challenge to effective usage of the spectrum. This is partly due to the rise in the use of license-free and managed shared bands as well as other opportunistic spectrum access solutions. As a result of this, the need for efficient spectrum usage schemes that are robust against interference has never been more important. In the past, most solutions to interference have addressed the problem by using avoidance techniques as well as mitigation approaches based on expert systems. More recently, researchers have successfully explored artificial intelligence/machine learning enabled physical layer techniques, especially deep learning which reduces or compensates for the interfering signal instead of simply avoiding it. In this paper, we address the knowledge gap in literature with respect to the state-of-the-art in deep learning-based interference suppression. Specifically, we review a wide range of techniques that have used deep learning to suppress interference by learning interference characteristics directly from data, rather than relying on expert systems. We provide a thorough technical discussion of the prominent deep learning algorithms that have been proposed in the literature and provide comparison and guidelines regarding their successful implementation in this application. In addition, we highlight challenges and potential future research directions for the successful adoption of deep learning in this critical field.

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“…Supervised machine learning has achieved widely successes in different domains [41,59,93,56,19,71,48,9,94,51]. Data labeling is the most important part of data preparation for supervised learning tasks.…”

Section: Motivation For Data Annotation and Labeling In Machine Learningmentioning

confidence: 99%

A Survey on Machine Learning Techniques for Auto Labeling of Video, Audio, and Text Data

Zhang,

Jafari,

Nagarkar

2021

Preprint

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Machine learning has been utilized to perform tasks in many different domains such as classification, object detection, image segmentation and natural language analysis. Data labeling has always been one of the most important tasks in machine learning. However, labeling large amounts of data increases the monetary cost in machine learning. As a result, researchers started to focus on reducing data annotation and labeling costs. Transfer learning was designed and widely used as an efficient approach that can reasonably reduce the negative impact of limited data, which in turn, reduces the data preparation cost. Even transferring previous knowledge from a source domain reduces the amount of data needed in a target domain. However, large amounts of annotated data are still demanded to build robust models and improve the prediction accuracy of the model. Therefore, researchers started to pay more attention on auto annotation and labeling. In this survey paper, we provide a review of previous techniques that focuses on optimized data annotation and labeling for video, audio, and text data.

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Transmitter Classification with Supervised Deep Learning

Cited by 37 publications

References 11 publications

Radio Frequency Fingerprint Identification Based on Denoising Autoencoders

Radio Frequency Fingerprint Identification Based on Denoising Autoencoders

Interference Suppression Using Deep Learning: Current Approaches and Open Challenges

A Survey on Machine Learning Techniques for Auto Labeling of Video, Audio, and Text Data

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