Classification of RF transmitters in the presence of multipath effects using CNN-LSTM

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dc.contributor.authorPatil, Pradnya
dc.contributor.authorWei, Zhuangkun
dc.contributor.authorPetrunin, Ivan
dc.contributor.authorGuo, Weisi
dc.date.accessioned2024-08-27T13:48:50Z
dc.date.available2024-08-27T13:48:50Z
dc.date.freetoread2024-08-27
dc.date.issued2024-06-09
dc.date.pubOnline2024-08-12
dc.description.abstractRadio frequency (RF) communication systems are the backbone of many intelligent transport and aerospace operations, ensuring safety, connectivity, and efficiency. Accurate classification of RF transmitters is vital to achieve safe and reliable functioning in various operational contexts. One challenge in RF classification lies in data drifting, which is particularly prevalent due to atmospheric and multipath effects. This paper provides a convolutional neural network based long short-term memory (CNN-LSTM) framework to classify the RF emitters in drift environments. We first simulate popular-used RF transmitters and capture the RF signatures, while considering both power amplifier dynamic imperfections and the multipath effects through wireless channel models for data drifting. To mitigate data drift, we extract the scattering coefficient and approximate entropy, and incorpo-rate them with the in-phase quadrature (I/Q) signals as the input to the CNN-LSTM classifier. This adaptive approach enables the model to adjust to environmental variations, ensuring sustained accuracy. Simulation results show the accuracy performance of the proposed CNN-LSTM classifier, which achieves an overall 91.11% in the presence of different multipath effects, bolstering the resilience and precision of realistic classification systems over state of the art ensemble voting approaches.
dc.description.conferencename2024 IEEE International Conference on Communications Workshops (ICC Workshops)
dc.description.sponsorshipEngineering and Physical Sciences Research Council
dc.description.sponsorshipThis work has been supported by the Engineering and Physical Sciences Research Council Trustworthy Autonomous Systems Security Node (EP/V026763/1), EPSRC CHED-DAR: Communications Hub For Empowering Distributed ClouD Computing Applications And Research (EP/X040518/1, EP/Y037421/1), and GE Aerospace.
dc.format.extentpp. 82-87
dc.identifier.citationPatil P, Wei Z, Petrunin I, Guo W. (2024) Classification of RF transmitters in the presence of multipath effects using CNN-LSTM. In: 2024 IEEE International Conference on Communications Workshops (ICC Workshops), Volume 113, 9-13 Jun 2024, Denver, CO, USA, pp. 82-87
dc.identifier.elementsID551784
dc.identifier.urihttps://doi.org/10.1109/iccworkshops59551.2024.10615420
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/22823
dc.identifier.volumeNo113
dc.language.isoen
dc.publisherIEEE
dc.publisher.urihttps://ieeexplore.ieee.org/document/10615420
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject4605 Data Management and Data Science
dc.subject46 Information and Computing Sciences
dc.subject40 Engineering
dc.titleClassification of RF transmitters in the presence of multipath effects using CNN-LSTM
dc.typeConference paper
dcterms.coverageDenver, USA
dcterms.temporal.endDate13 Jun 2024
dcterms.temporal.startDate9 Jun 2024

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