dc.contributor.author | Krayani, A | |
dc.contributor.author | Alam, AS | |
dc.contributor.author | Marcenaro, L | |
dc.contributor.author | Nallanathan, A | |
dc.contributor.author | Regazzoni, C | |
dc.date.accessioned | 2023-11-09T10:39:29Z | |
dc.date.available | 2023-11-09T10:39:29Z | |
dc.date.issued | 2022 | |
dc.identifier.issn | 0018-9545 | |
dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/91813 | |
dc.description.abstract | The integration of Cognitive Radio (CR) with Unmanned Aerial Vehicles (UAVs) is an effective step towards relieving the spectrum scarcity and empowering the UAV with a high degree of intelligence. The dynamic nature of CR and the dominant line-of-sight links of UAVs poses serious security challenges and make the CR-UAV prone to a variety of attacks as malicious jamming. Joint jammer detection and automatic jammer classification is a powerful approach against the physical layer threats by identifying multiple jammers attacking the network that realize a crucial stage towards efficient interference management. This paper proposes a novel method for joint detection and automatic classification of multiple jammers attacking with different modulation schemes. The method is based on learning a representation of the radio environment encoded in a Generalized Dynamic Bayesian Network (GDBN) whilst multiple GDBN models represent various jamming signals under different modulation schemes. The CR-UAV performs multiple predictions online in parallel and evaluates multiple abnormality measurements based on a Modified Markov Jump Particle Filter (M-MJPF) to select the best-fit model that explains the detected jammer and recognize the modulation scheme accordingly. The simulated results demonstrate that the proposed GDBN-based method outperforms Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN) and Stacked Autoencoder (SAE) in terms of classification accuracy and achieves a higher degree of explainability of its own decisions by interpreting causes and effects at hierarchical levels under the Bayesian learning and reasoning processes. | en_US |
dc.format.extent | 12972 - 12988 | |
dc.publisher | IEEE | en_US |
dc.relation.ispartof | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY | |
dc.subject | Bayesian filtering | en_US |
dc.subject | cognitive radio | en_US |
dc.subject | dynamic Bayesian network | en_US |
dc.subject | modulation recognition | en_US |
dc.subject | unmanned aerial vehicles | en_US |
dc.title | Automatic Jamming Signal Classification in Cognitive UAV Radios | en_US |
dc.type | Article | en_US |
dc.rights.holder | © 2022 Published by IEEE | |
dc.identifier.doi | 10.1109/TVT.2022.3199038 | |
pubs.author-url | https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000908826000042&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=612ae0d773dcbdba3046f6df545e9f6a | en_US |
pubs.issue | 12 | en_US |
pubs.notes | Not known | en_US |
pubs.publication-status | Published | en_US |
pubs.volume | 71 | en_US |
rioxxterms.funder | Default funder | en_US |
rioxxterms.identifier.project | Default project | en_US |