Deep-Learning-Based ModCod Predictor for Satellite Channels

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dc.contributor.authorMakara, A. L.
dc.contributor.authorCsurgai-Horvath, L.
dc.coverage.issue1cs
dc.coverage.volume33cs
dc.date.accessioned2024-05-28T12:43:33Z
dc.date.available2024-05-28T12:43:33Z
dc.date.issued2024-04cs
dc.description.abstractOne of the significant challenges for satellite communications is to serve the ever-increasing demand for the use of finite resources. One option is to increase channel utilization, i.e., to transmit as much data as possible in a given frequency range. Since the channel is highly variable, primarily due to the ionosphere and troposphere, this goal can only be achieved by adaptively varying modulation and coding schemes. Most procedures and algorithms estimate the channel characteristics and descriptive quantities (e.g., signal-to-noise ratio). Ultimately, these procedures solve a regression problem. The resulting quantity is used as the basis for a decision process. Since valuation can also be subject to error, the decision mechanisms based on it must compensate and mitigate this error. The main element of the current research is to combine these two steps and solve them together using deep neural networks. The theoretical advantages of the method include that a better result can be achieved by having a joint estimation and decision process with a standard algorithm and cost function. The theoretical approach was tested with an actual protocol -- Digital Video Broadcasting - Satellite - Second Generation -- where we observed a significant improvement in channel utilization on previously recorded Alphasat satellite data.en
dc.formattextcs
dc.format.extent182-194cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationRadioengineering. 2024 vol. 33, iss. 1, s. 182-194. ISSN 1210-2512cs
dc.identifier.doi10.13164/re.2024.0182en
dc.identifier.issn1210-2512
dc.identifier.urihttps://hdl.handle.net/11012/245658
dc.language.isoencs
dc.publisherSpolečnost pro radioelektronické inženýrstvícs
dc.relation.ispartofRadioengineeringcs
dc.relation.urihttps://www.radioeng.cz/fulltexts/2024/24_01_0182_0194.pdfcs
dc.rightsCreative Commons Attribution 4.0 International licenseen
dc.rights.accessopenAccessen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectSatellite linksen
dc.subjecttime series predictionen
dc.subjectimbalanced classificationen
dc.subjectsupervised machine learningen
dc.subjectfadingen
dc.subjectACMen
dc.subjectAIen
dc.subjectDLen
dc.subjectDNNen
dc.subjectDVB-S2en
dc.subjectAlphasaten
dc.subjectsatellite channelen
dc.subjectQ banden
dc.subjectLSTMen
dc.subjectDNNen
dc.subjectsatellite-Earth communicationen
dc.subjectwireless signal propagationen
dc.titleDeep-Learning-Based ModCod Predictor for Satellite Channelsen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.facultyFakulta eletrotechniky a komunikačních technologiícs
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