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Quality-of-Service Prediction for Physical-layer Security via Secrecy Maps

: Gutierrez-Estevez, M.A.; Utkovski, Z.; Agostini, P.; Schäufele, D.; Frey, M.; Bjelakovic, I.; Stanczak, S.


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Computer Society; IEEE Signal Processing Society:
IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2020. Proceedings : May 4-8, 2020, Barcelona, Spain
Piscataway, NJ: IEEE, 2020
ISBN: 978-1-5090-6631-5
ISBN: 978-1-5090-6632-2
International Conference on Acoustics, Speech and Signal Processing (ICASSP) <45, 2020, Barcelona>
Fraunhofer HHI ()

While most of the theoretical aspects of physical layer security are well understood, practical applications lag substantially behind theoretical advances. As a step towards the integration of physical-layer security aspects in the radio access system design, the concept of secrecy maps has been recently introduced. Building upon this concept, in this work we focus on system design-related aspects which consider physical-layer security as a service, together with thereby associated secrecy-related Quality-of-Service (QoS) requirements. We adopt a statistical learning framework to characterize the wireless environment from the perspective of semantic security via QoS maps that indicate locations in the wireless environment that surmount a QoS-related performance threshold with a guaranteed level of confidence. To provide secrecy characterization of a particular wireless environment, we propose to learn any-to-any QoS maps by leveraging the tensor completion framework. We demonstrate the advantage of the proposed method over a baseline algorithm based on tensor rank minimization via numerical simulations.