A Framework for Analyzing Spectrum Characteristics in Large Spatio-temporal Scales

Zeng, Yijing; Chandrasekaran, Varun; Banerjee, Suman; Giustiniano, Domenico

doi:10.1145/3300061.3345450

Cited by 12 publications

(2 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Existing intelligent spectrum works range from radio observatories and test-beds that monitor and provide high-level insights on a large geographical scale [ 25 , 26 , 27 , 28 ], to systems for radio anomaly detection and device fingerprinting [ 29 , 30 , 31 , 32 , 33 ], and various aspects of waveform classification [ 9 , 10 , 34 , 35 , 36 , 37 , 38 , 39 ].…”

Section: Introductionmentioning

confidence: 99%

Robust Computationally-Efficient Wireless Emitter Classification Using Autoencoders and Convolutional Neural Networks

Almazrouei

Gianini

Almoosa

et al. 2021

Sensors

View full text Add to dashboard Cite

This paper proposes a novel Deep Learning (DL)-based approach for classifying the radio-access technology (RAT) of wireless emitters. The approach improves computational efficiency and accuracy under harsh channel conditions with respect to existing approaches. Intelligent spectrum monitoring is a crucial enabler for emerging wireless access environments that supports sharing of (and dynamic access to) spectral resources between multiple RATs and user classes. Emitter classification enables monitoring the varying patterns of spectral occupancy across RATs, which is instrumental in optimizing spectral utilization and interference management and supporting efficient enforcement of access regulations. Existing emitter classification approaches successfully leverage convolutional neural networks (CNNs) to recognize RAT visual features in spectrograms and other time-frequency representations; however, the corresponding classification accuracy degrades severely under harsh propagation conditions, and the computational cost of CNNs may limit their adoption in resource-constrained network edge scenarios. In this work, we propose a novel emitter classification solution consisting of a Denoising Autoencoder (DAE), which feeds a CNN classifier with lower dimensionality, denoised representations of channel-corrupted spectrograms. We demonstrate—using a standard-compliant simulation of various RATs including LTE and four latest Wi-Fi standards—that in harsh channel conditions including non-line-of-sight, large scale fading, and mobility-induced Doppler shifts, our proposed solution outperforms a wide range of standalone CNNs and other machine learning models while requiring significantly less computational resources. The maximum achieved accuracy of the emitter classifier is 100%, and the average accuracy is 91% across all the propagation conditions.

show abstract

Section: Introductionmentioning

confidence: 99%

Robust Computationally-Efficient Wireless Emitter Classification Using Autoencoders and Convolutional Neural Networks

Almazrouei

Gianini

Almoosa

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…While there is a few works in the literature based on crowd-sourced transmitter localization [5]- [9], unfortunately, their application is limited to very specific, in fact unrealistic, cases where the transmitters are separated with a significant distance of tens or hundreds of meters and all the radio channel characteristics as well as the number of transmitters and their transmit power values are known at the localization entity. More generic frameworks such as BigSpec [10], similar to DeepTxFinder, strive for scalability as spectrum sensors generate a massive amount of data that has to be processed in near-real time. The closest works to ours are [11] and [12] which operate without knowing the number transmitters and their transmission powers.…”

Section: Introductionmentioning

confidence: 99%

DeepTxFinder: Multiple Transmitter Localization by Deep Learning in Crowdsourced Spectrum Sensing

Zubow

Bayhan

Gawłowicz

et al. 2020

2020 29th International Conference on Computer Communications and Networks (ICCCN)

View full text Add to dashboard Cite

As the radio spectrum has become the bottleneck resource with increasing volume of mobile data and ultra-dense network deployments, it is crucial to use spectrum more flexibly in time, space, and frequency dimensions. However, higher efficiency in spectrum usage facilitated by flexible spectrum allocation comes with a cost, namely the increased complexity of spectrum monitoring and management. Identifying the transmitters is at the interest of particularly spectrum enforcement authorities to ensure that spectrum is used as intended by the legitimate users of the spectrum. For a scalable, efficient, and highly-accurate operation, we propose a crowd-sensing based solution where sensing devices report their measured receive power levels to a central entity which later fuses the collected information for localizing an unknown number of transmitters. Our solution, referred to as DeepTxFinder, leverages deep learning to handle many sources of uncertainty in the operation environment: namely number of transmitters, their transmission power levels, and channel conditions (shadowing). Using deep-learning, DeepTxFinder distinguishes itself from the prior state-of-the art which requires knowledge of the number and transmission power of transmitters or require the transmitters to be well separated in space by tens to hundreds of meters making them ill-suited for application in expected ultra-dense deployment of smallcells. Moreover, we propose a tiling-based approach to increase the scalability of our proposal by reducing the computational complexity. Our simulation studies show that DeepTxFinder can provide a high detection accuracy even only by collecting data from a very small number of sensors. More specifically, with 1 %-2 % sensor density DeepTxFinder can estimate the number of transmitters and their locations with high probability which proves that sparse sensing is feasible.

show abstract

Large Scale Collaborative Detection and Location of Threats in the Electromagnetic Space

Giustiniano

Lenders²,

Pollin

2020

NATO Science for Peace and Security Series B: Physics and Biophysics

View full text Add to dashboard Cite

A Framework for Analyzing Spectrum Characteristics in Large Spatio-temporal Scales

Cited by 12 publications

References 43 publications

Robust Computationally-Efficient Wireless Emitter Classification Using Autoencoders and Convolutional Neural Networks

Robust Computationally-Efficient Wireless Emitter Classification Using Autoencoders and Convolutional Neural Networks

DeepTxFinder: Multiple Transmitter Localization by Deep Learning in Crowdsourced Spectrum Sensing

Large Scale Collaborative Detection and Location of Threats in the Electromagnetic Space

Contact Info

Product

Resources

About