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Waveform Classification in Radar-Communications Coexistence Scenarios

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Kong, Gyuyeol
dc.contributor.author Jung, Minchae
dc.contributor.author Koivunen, Visa
dc.date.accessioned 2021-03-31T06:12:37Z
dc.date.available 2021-03-31T06:12:37Z
dc.date.issued 2020
dc.identifier.citation Kong , G , Jung , M & Koivunen , V 2020 , Waveform Classification in Radar-Communications Coexistence Scenarios . in 2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings . , 9322442 , IEEE Global Communications Conference , IEEE , IEEE Global Communications Conference , Taipei , Taiwan, Republic of China , 07/12/2020 . https://doi.org/10.1109/GLOBECOM42002.2020.9322442 en
dc.identifier.isbn 9781728182988
dc.identifier.issn 2334-0983
dc.identifier.issn 2576-6813
dc.identifier.other PURE UUID: 277a9709-eb48-4dd9-a7dc-09141d7a3478
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/277a9709-eb48-4dd9-a7dc-09141d7a3478
dc.identifier.other PURE LINK: http://www.scopus.com/inward/record.url?scp=85100434555&partnerID=8YFLogxK
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/61182820/Kong_Waveform_classification_in_radar_communications_Globecom2020.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/103384
dc.description.abstract In this paper the problem of recognizing waveform and modulation is addressed in radar-communications coexistence and shared spectrum scenarios. We propose a deep learning method for waveform classification. A hierarchical recognition approach is employed. The received complex-valued signal is first classified to single carrier radar, communication or multicarrier waveforms. Fourier synchrosqueezing transformation (FSST) time-frequency representation is computed and used as an input to a convolutional neural network (CNN). For multicarrier signals, key waveform parameters including the cyclic prefix (CP) duration, number of subcarriers and subcarrier spacing are estimated. The modulation type used for subcarriers is recognized. Independent component analysis (ICA) is used to enforce independence of I- and Q-components, and consequently significantly improving the classification performance. Simulation results demonstrate the high classification performance of the proposed method even for orthogonal frequency division multiplexing (OFDM) signals with high-order quadrature amplitude modulation (QAM). en
dc.format.extent 6
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation.ispartof IEEE Global Communications Conference en
dc.relation.ispartofseries 2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings en
dc.relation.ispartofseries IEEE Global Communications Conference en
dc.rights openAccess en
dc.title Waveform Classification in Radar-Communications Coexistence Scenarios en
dc.type A4 Artikkeli konferenssijulkaisussa fi
dc.description.version Peer reviewed en
dc.contributor.department Dept Signal Process and Acoust
dc.contributor.department Virginia Polytechnic Institute and State University
dc.subject.keyword convolutional neural network
dc.subject.keyword Fourier synchrosqueezing transform
dc.subject.keyword independent component analysis
dc.subject.keyword Signal intelligence
dc.subject.keyword waveform recognition
dc.identifier.urn URN:NBN:fi:aalto-202103312657
dc.identifier.doi 10.1109/GLOBECOM42002.2020.9322442
dc.type.version acceptedVersion

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