A novel demodulation system for base band digital modulation signals based on the deep long short-term memory model

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dc.authorid0000-0001-5256-7648en_US
dc.authorid0000-0003-1614-2639en_US
dc.authorid0000-0001-7345-2727
dc.authorid0000-0003-1840-9958
dc.contributor.authorDaldal, Nihat
dc.contributor.authorŞengür, Abdulkadir
dc.contributor.authorPolat, Kemal
dc.contributor.authorCömert, Zafer
dc.date.accessioned2021-06-23T19:53:58Z
dc.date.available2021-06-23T19:53:58Z
dc.date.issued2020
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractFor high-frequency digital signals to be transmitted over long distances, the basic digital signal needs to be modulated with a high-frequency carrier. In this case, the baseband digital signal is called the pass-band signal. In particular, in wireless communication systems or ultraviolet or infrared communications, transitional band digital modulations are used. The most commonly used transition band modulations are ASK (Amplitude Shift Keying), FSK (Frequency Shift Keying) and PSK (Phase Shift Keying) modulations. In this study, 8 bits of all digital baseband data were obtained from the transition band modulations in ASK, FSK and PSK modulations in MATLAB. Also, the noises ranging from 5 dB to 25 dB were added to these ASK, FSK, and PSK modulations. The originality of this paper is to a single deep learning model to demodulate the ASK, FSK, and PSK modulations by using a data-driven approach. The main aim is to demodulate the baseband numerical data from the transition band noised modulation signals instead of the hardware demodulator circuits. For this aim, the noised modulated signals were applied to deep LSTM (Long short-term memory) model without feature extraction. The performance measures to evaluate the proposed deep learning-based demodulator method have been used, and they are MAPE, MSE, R-2, RMSE, and NRMSE. The obtained MAPE demodulation results for the worst case of ASK, FSK, and PSK (added 5 dB to these modulations) are 4.392, 5.60, and 3.166, respectively. The experimental results have demonstrated that the proposed LSTM demodulator model could be used safely in the demodulation of ASK, FSK, and PSK modulations in the real world. (C) 2020 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.apacoust.2020.107346
dc.identifier.issn0003-682X
dc.identifier.issn1872-910X
dc.identifier.scopus2-s2.0-85082863356en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.apacoust.2020.107346
dc.identifier.urihttps://hdl.handle.net/20.500.12491/10361
dc.identifier.volume166en_US
dc.identifier.wosWOS:000536142500032en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorDaldal, Nihat
dc.institutionauthorPolat, Kemal
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofApplied Acousticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDeep Learningen_US
dc.subjectAmplitude Shift Keyingen_US
dc.subjectFrequency Shift Keyingen_US
dc.subjectPhase Shift Keyingen_US
dc.subjectCommunication Demodulationen_US
dc.titleA novel demodulation system for base band digital modulation signals based on the deep long short-term memory modelen_US
dc.typeArticleen_US

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