Spectrum Monitoring for Radar Bands Using Deep Convolutional Neural Networks

Selim, Ahmed; Paisana, Francisco; Arokkiam, Jerome A.; Zhang, Yi; Doyle, Linda; DaSilva, Luiz A.

doi:10.1109/glocom.2017.8254105

Cited by 85 publications

(39 citation statements)

References 11 publications

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“…One of the pioneers in the domain were the authors of [9], who demonstrated that CNNs trained on time domain IQ data significantly outperform traditional approaches for automatic modulation recognition based on expert features such as cyclic-moment based features, and conventional classifiers such as decision trees, k-NNs, SVMs, NN and Naive Bayes. Selim et al [10] propose to use amplitude and phase difference data to train CNN classifiers able to detect the presence of radar signals with high accuracy. Akeret at al.…”

Section: B Related Workmentioning

confidence: 99%

“…The first, is a real-valued equivalent of the raw complex temporal wireless signal inspired by the results in [9]. The second, is based on the amplitude and phase of the raw wireless signal, similar to the one used in the work of Selim et al [10] for identifying radar signals. The last is a frequency domain representation inspired by the work of Danev et al [28] which showed that frequency-based features outperform their timebased equivalents for wireless device identification.…”

Section: Wireless Signal Representationmentioning

confidence: 99%

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End-to-End Learning From Spectrum Data: A Deep Learning Approach for Wireless Signal Identification in Spectrum Monitoring Applications

et al. 2018

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This paper presents end-to-end learning from spectrum data -an umbrella term for new sophisticated wireless signal identification approaches in spectrum monitoring applications based on deep neural networks. End-to-end learning allows to (i) automatically learn features directly from simple wireless signal representations, without requiring design of hand-crafted expert features like higher order cyclic moments, and (ii) train wireless signal classifiers in one end-to-end step which eliminates the need for complex multi-stage machine learning processing pipelines. The purpose of this article is to present the conceptual framework of end-to-end learning for spectrum monitoring and systematically introduce a generic methodology to easily design and implement wireless signal classifiers. Furthermore, we investigate the importance of the choice of wireless data representation to various spectrum monitoring tasks. In particular, two case studies are elaborated (i) modulation recognition and (ii) wireless technology interference detection. For each case study three convolutional neural networks are evaluated for the following wireless signal representations: temporal IQ data, the amplitude/phase representation and the frequency domain representation. From our analysis we prove that the wireless data representation impacts the accuracy depending on the specifics and similarities of the wireless signals that need to be differentiated, with different data representations resulting in accuracy variations of up to 29%. Experimental results show that using the amplitude/phase representation for recognizing modulation formats can lead to performance improvements up to 2% and 12% for medium to high SNR compared to IQ and frequency domain data, respectively. For the task of detecting interference, frequency domain representation outperformed amplitude/phase and IQ data representation up to 20%.

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Section: B Related Workmentioning

confidence: 99%

Section: Wireless Signal Representationmentioning

confidence: 99%

End-to-End Learning From Spectrum Data: A Deep Learning Approach for Wireless Signal Identification in Spectrum Monitoring Applications

et al. 2018

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“…Machine learning (ML) techniques have shown great promise in image and speech identification problems, and are steadily gaining traction in applications within the wireless domain. ORACLE is solely built on a convolutional neural network architecture that has not only seen success in the above areas, but has also been previously used for modulation [2] and protocol identification [3]. ORACLE adopts a stagewise approach towards achieving practical classification.…”

Section: B Machine Learning For Rf Fingerprinting In Oraclementioning

confidence: 99%

ORACLE: Optimized Radio clAssification through Convolutional neuraL nEtworks

Sankhe

Belgiovine

Zhou

et al. 2019

IEEE INFOCOM 2019 - IEEE Conference on Computer Communications

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This paper describes the architecture and performance of ORACLE, an approach for detecting a unique radio from a large pool of bit-similar devices (same hardware, protocol, physical address, MAC ID) using only IQ samples at the physical layer. ORACLE trains a convolutional neural network (CNN) that balances computational time and accuracy, showing 99% classification accuracy for a 16-node USRP X310 SDR testbed and an external database of >100 COTS WiFi devices. Our work makes the following contributions: (i) it studies the hardwarecentric features within the transmitter chain that causes IQ sample variations; (ii) for an idealized static channel environment, it proposes a CNN architecture requiring only raw IQ samples accessible at the front-end, without channel estimation or prior knowledge of the communication protocol; (iii) for dynamic channels, it demonstrates a principled method of feedback-driven transmitter-side modifications that uses channel estimation at the receiver to increase differentiability for the CNN classifier.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 nearperfect device classification accuracy.

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“…In [163], an SVM solution is developed to classify interference in wireless sensor networks (WSNs) from IEEE 802.11 signals and microwave ovens. A recent work [164] shows the use of DCNNs to classify radar signals using both spectrogram and amplitude-phase representations of the received signal. In [165], DCNN models are proposed to accomplish interference classification on two-dimensional time-frequency representations of the received signal to mitigate the effects of radio interference in cosmological data.…”

Section: Wireless Interference Classificationmentioning

confidence: 99%

Machine learning for wireless communications in the Internet of Things: A comprehensive survey

et al. 2019

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The Internet of Things (IoT) is expected to require more effective and efficient wireless communications than ever before. For this reason, techniques such as spectrum sharing, dynamic spectrum access, extraction of signal intelligence and optimized routing will soon become essential components of the IoT wireless communication paradigm. In this vision, IoT devices must be able to not only learn to autonomously extract spectrum knowledge on-the-fly from the network but also leverage such knowledge to dynamically change appropriate wireless parameters (e.g., frequency band, symbol modulation, coding rate, route selection, etc.) to reach the network's optimal operating point. Given that the majority of the IoT will be composed of tiny, mobile, and energy-constrained devices, traditional techniques based on a priori network optimization may not be suitable, since (i) an accurate model of the environment may not be readily available in practical scenarios; (ii) the computational requirements of traditional optimization techniques may prove unbearable for IoT devices. To address the above challenges, much research has been devoted to exploring the use of machine learning to address problems in the IoT wireless communications domain. The reason behind machine learning's popularity is that it provides a general framework to solve very complex problems where a model of the phenomenon being learned is too complex to derive or too dynamic to be summarized in mathematical terms.This work provides a comprehensive survey of the state of the art in the application of machine learning techniques to address key problems in IoT wireless communications with an emphasis on its ad hoc networking aspect. First, we present extensive background notions of machine learning techniques. Then, by adopting a bottom-up approach, we examine existing work on machine learning for the IoT at the physical, data-link and network layer of the protocol stack. Thereafter, we discuss directions taken by the community towards hardware implementation to ensure the feasibility of these techniques. Additionally, before concluding, we also provide a brief discussion of the application of machine learning in IoT beyond wireless communication. Finally, each of these discussions is accompanied by a detailed analysis of the related open problems and challenges.

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Spectrum Monitoring for Radar Bands Using Deep Convolutional Neural Networks

Cited by 85 publications

References 11 publications

End-to-End Learning From Spectrum Data: A Deep Learning Approach for Wireless Signal Identification in Spectrum Monitoring Applications

End-to-End Learning From Spectrum Data: A Deep Learning Approach for Wireless Signal Identification in Spectrum Monitoring Applications

ORACLE: Optimized Radio clAssification through Convolutional neuraL nEtworks

Machine learning for wireless communications in the Internet of Things: A comprehensive survey

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