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zubow2020deeptxfinder


Anatolij Zubow, Suzan Bayhan, Piotr Gawłowicz and Falko Dressler, "DeepTxFinder: Multiple Transmitter Localization by Deep Learning in Crowdsourced Spectrum Sensing," Proceedings of IEEE International Conference on Computer Communication and Networks (ICCCN 2020), Virtual Conference, August 2020.

Abstract

As the radio spectrum has become the bottleneck re- source with increasing volume of mobile data and ultra-dense net- work 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 lev- els, 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 small- cells. 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.

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Anatolij Zubow
Suzan Bayhan
Piotr Gawłowicz
Falko Dressler

BibTeX reference

@inproceedings{zubow2020deeptxfinder,
    author = {Zubow, Anatolij and Bayhan, Suzan and Gawłowicz, Piotr and Dressler, Falko},
    doi = {10.1109/ICCCN49398.2020.9209727},
    title = {{DeepTxFinder: Multiple Transmitter Localization by Deep Learning in Crowdsourced Spectrum Sensing}},
    publisher = {IEEE},
    address = {Virtual Conference},
    booktitle = {IEEE International Conference on Computer Communication and Networks (ICCCN 2020)},
    month = {8},
    year = {2020},
   }
   
   

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