Anomaly Detection with Autoencoders for Spectrum Sharing and Monitoring

Tschimben, Stefan; Gifford, Kevin

doi:10.1109/cqr54764.2022.9918589

Cited by 3 publications

(1 citation statement)

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“…In this particular context, the concept of radio dynamic zones (RDZs) emerges as a new concept [2]- [4], where geographical areas with dedicated spectrum resources are effec-This work is supported in part by the NSF PAWR award CNS-1939334 and its associated supplement for studying National Radio Dynamic Zones (NRDZs). The authors would like to thank Wireless Research Center for measuring antenna patterns by using an anechoic chamber.…”

Section: Introductionmentioning

confidence: 99%

National Radio Dynamic Zone Concept with Autonomous Aerial and Ground Spectrum Sensors

Maeng

Güvenç

Sichitiu

et al. 2022

2022 IEEE International Conference on Communications Workshops (ICC Workshops)

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Radio dynamic zones (RDZs) are geographical areas within which dedicated spectrum resources are monitored and controlled to enable the development and testing of new spectrum technologies. Real-time spectrum awareness within an RDZ is critical for preventing interference with nearby incumbent users of the spectrum. In this paper, we consider a 3D RDZ scenario and propose to use unmanned aerial vehicles (UAVs) equipped with spectrum sensors to create and maintain a 3D radio map of received signal power from different sources within the RDZ. In particular, we introduce a 3D Kriging interpolation technique that uses realistic 3D correlation models of the signal power extracted from extensive measurements carried out at the NSF AERPAW platform. Using C-Band signal measurements by a UAV at altitudes between 30 m-110 m, we first develop realistic propagation models on air-to-ground path loss, shadowing, spatial correlation, and semi-variogram, while taking into account the knowledge of antenna radiation patterns and ground reflection. Subsequently, we generate a 3D radio map of a signal source within the RDZ using the Kriging interpolation and evaluate its sensitivity to the number of measurements used and their spatial distribution. Our results show that the proposed 3D Kriging interpolation technique provides significantly better radio maps when compared with an approach that assumes perfect knowledge of path loss.

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Section: Introductionmentioning

confidence: 99%