A novel demodulation structure for quadrate modulation signals using the segmentary neural network modelling

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dc.authorid0000-0001-7345-2727
dc.authorid0000-0003-1840-9958
dc.contributor.authorDaldal, Nihat
dc.contributor.authorNour, Majid
dc.contributor.authorPolat, Kemal
dc.date.accessioned2021-06-23T19:54:08Z
dc.date.available2021-06-23T19:54:08Z
dc.date.issued2020
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractIn digital communication, the baseband information signal is modulated by the high-frequency carrier to produce a passband signal and applied to the transmission line. QASK (quadrature amplitude-shift keying), QFSK (quadrature frequency-shift keying), and QPSK (quadrature phase-shift keying) modulations from Quadrate type digital modulations are used for high-speed communication in passband digital modulations. They are a widespread modulation for fast and easy data transmission, especially in wireless communication and modem devices. In these modulations, four separate carriers are used, and since each carrier represents 2 bits, the transmission rate doubles compared to conventional digital modulation. This study aims to obtain the baseband signal from quadrate type modulation signals. For this purpose, all 8-bit data between Decimal 0-255 were obtained in QASK, QFSK, and QPSK modulations and signal matrices were formed. The various SNR (signal to noise ratio) values of 5 dB-10 dB-15 dB-20 dB noise were added to these signals to examine the system performance during the modulated signal transmission. The generated signals were given to the demodulation system developed using different methods and tested. The best results were obtained in developed a segmentary NN (Neural Network). In this study, it was observed that modulation signal matrices were given directly to an ANN (Artificial Neural Network) and that the results could not be predicted with the application of noise matrices for testing. Then the modulation signals with the proposed method are divided into four parts. Each segment represents a two-bit piece of data. In the case of a column matrix, four parts were applied as input to the neural network model. In the output of ANN, the result matrix to be predicted is created. Each modulation signal applied to the network input was classified between 0 and 3 at the output. Modulation data-carrying 8 bits are applied to the network in 4 steps and classified. 4 separate classification data from the ANN output is converted back to 2-bit logic. Therefore, signals carrying 8 bits of data are obtained in 4 steps. After the formation of the ANN network, baseband digital signal estimation was performed quickly in 4 steps across each byte modulation signals under different noises coming into the network, and demodulation data was successfully achieved. (C) 2020 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.apacoust.2020.107251
dc.identifier.issn0003-682X
dc.identifier.issn1872-910X
dc.identifier.scopus2-s2.0-85079331245en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.apacoust.2020.107251
dc.identifier.urihttps://hdl.handle.net/20.500.12491/10432
dc.identifier.volume164en_US
dc.identifier.wosWOS:000523606100035en_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.subjectQuadrate Modulation Signalsen_US
dc.subjectAdaptive Quadrate Demodulationen_US
dc.subjectSegmentary Neural Networken_US
dc.titleA novel demodulation structure for quadrate modulation signals using the segmentary neural network modellingen_US
dc.typeArticleen_US

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