Electrosense: Open and Big Spectrum Data

Rajendran, Sreeraj; Calvo-Palomino, Roberto; Fuchs, Markus; Bergh, Bertold Van den; Cordobés, Héctor; Giustiniano, Domenico; Pollin, Sofie; Lenders, Vincent

doi:10.1109/mcom.2017.1700200

Cited by 128 publications

(83 citation statements)

References 14 publications

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“…In order to limit the data overhead caused by huge amounts of I and Q samples that are generated by monitoring devices, the predictive models can be pushed to the end devices itself. Recently, [32] proposed Electrosense, an initiative for large-scale spectrum monitoring in different regions of the world using low-cost sensors and providing the processed spectrum data as open spectrum data. Access to large datasets is crucial for evaluating research advances and enabling a playground for wireless communication researchers interested to acquire a deeper knowledge of spectrum usage and to extract meaningful knowledge that can be used to design better wireless communication systems.…”

Section: A Scalable Spectrum Monitoringmentioning

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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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: A Scalable Spectrum Monitoringmentioning

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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“…These wide ranging infrastructure problems were systematically analyzed and partially solved by the Electrosense 1 [1] platform. Electrosense is interdisciplinary and combines the power of crowdsourcing with Big data to solve the wireless spectrum monitoring problem.…”

Section: Introductionmentioning

confidence: 99%

SAIFE: Unsupervised Wireless Spectrum Anomaly Detection with Interpretable Features

Rajendran

Meert

Lenders³

et al. 2018

2018 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN)

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Detecting anomalous behavior in wireless spectrum is a demanding task due to the sheer complexity of the electromagnetic spectrum use. Wireless spectrum anomalies can take a wide range of forms from the presence of an unwanted signal in a licensed band to the absence of an expected signal, which makes manual labeling of anomalies difficult and suboptimal. We present, Spectrum Anomaly Detector with Interpretable FEatures (SAIFE), an Adversarial Autoencoder (AAE) based anomaly detector for wireless spectrum anomaly detection using Power Spectral Density (PSD) data which achieves good anomaly detection and localization in an unsupervised setting. In addition, we investigate the model's capabilities to learn interpretable features such as signal bandwidth, class and center frequency in a semi-supervised fashion. Along with anomaly detection the model exhibits promising results for lossy PSD data compression up to 120X and semi-supervised signal classification accuracy close to 100% on three datasets just using 20% labeled samples. Finally the model is tested on data from one of the distributed Electrosense sensors over a long term of 500 hours showing its anomaly detection capabilities.

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“…Furthermore, the data costs related to the transfer and storage of the spectrum sensing sensor data is huge. For instance, the crowdsourced spectrum sensing framework, Electrosense [14], enables three pipelines with very low, medium and high data transfer costs namely: Feature, Power Spectral Density (PSD) and In-phase and quadrature phase (IQ) pipeline respectively. For fulfilling the long term spectrum monitoring dream, it is essential to extract critical features and employ the feature pipeline whenever possible.…”

Section: A Challenge 1: Finding An Interpretable Feature Spacementioning

confidence: 99%

Crowdsourced Wireless Spectrum Anomaly Detection

Rajendran

Lenders²,

Meert

et al. 2020

IEEE Trans. Cogn. Commun. Netw.

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Automated wireless spectrum monitoring across frequency, time and space will be essential for many future applications. Manual and fine-grained spectrum analysis is becoming impossible because of the large number of measurement locations and complexity of the spectrum use landscape. Detecting unexpected behaviors in the wireless spectrum from the collected data is a crucial part of this automated monitoring, and the control of detected anomalies is a key functionality to enable interaction between the automated system and the end user. In this paper we look into the wireless spectrum anomaly detection problem for crowdsourced sensors. We first analyze in detail the nature of these anomalies and design effective algorithms to bring the higher dimensional input data to a common feature space across sensors. Anomalies can then be detected as outliers in this feature space. In addition, we investigate the importance of user feedback in the anomaly detection process to improve the performance of unsupervised anomaly detection. Furthermore, schemes for generalizing user feedback across sensors are also developed to close the anomaly detection loop.

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Electrosense: Open and Big Spectrum Data

Cited by 128 publications

References 14 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

SAIFE: Unsupervised Wireless Spectrum Anomaly Detection with Interpretable Features

Crowdsourced Wireless Spectrum Anomaly Detection

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