Frame Detection with Deep Learning
| dc.contributor.author | Yıldırım, Mete | |
| dc.contributor.author | Sokullu, Radosveta | |
| dc.date.accessioned | 2023-01-12T20:29:17Z | |
| dc.date.available | 2023-01-12T20:29:17Z | |
| dc.date.issued | 2021 | |
| dc.department | N/A/Department | en_US |
| dc.description.abstract | Deep learning has become a way of solution for the realization of complex computations. As electronic communication starts to use more complex channels, the systems need to handle tough computations. For this reason, research on the use of deep learning in communication has increased recently. These researches aim to realize many applications used in communication with deep learning. Frame detection is one of the first things a receiver must handle and it may require a lot of hard computations. Deep learning-based frame detection can be an alternative approach. This study aims to build models that perform frame detection with deep learning. The proposed models provide the performance of correlation-based frame receivers commonly used for frame detection. The mean square root error of the prediction deviation is used as an evaluation metric to compare the proposed model to classic systems. | en_US |
| dc.identifier.doi | 10.18466/cbayarfbe.693942 | |
| dc.identifier.endpage | 213 | en_US |
| dc.identifier.issn | 1305-130X | |
| dc.identifier.issn | 1305-1385 | |
| dc.identifier.issue | 2 | en_US |
| dc.identifier.startpage | 209 | en_US |
| dc.identifier.trdizinid | 493704 | en_US |
| dc.identifier.uri | https://doi.org/10.18466/cbayarfbe.693942 | |
| dc.identifier.uri | https://search.trdizin.gov.tr/yayin/detay/493704 | |
| dc.identifier.uri | https://hdl.handle.net/11454/80419 | |
| dc.identifier.volume | 17 | en_US |
| dc.indekslendigikaynak | TR-Dizin | en_US |
| dc.language.iso | en | en_US |
| dc.relation.ispartof | Celal Bayar Üniversitesi Fen Bilimleri Dergisi | en_US |
| dc.relation.publicationcategory | Makale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.subject | correlator | en_US |
| dc.subject | deep learning | en_US |
| dc.subject | Communication | en_US |
| dc.subject | neural network | en_US |
| dc.subject | frame detection | en_US |
| dc.title | Frame Detection with Deep Learning | en_US |
| dc.type | Article | en_US |
