Channel Estimation with Fully Connected Deep Neural Network
| dc.authorid | Sokullu, Radosveta/0000-0002-3544-0319 | |
| dc.authorscopusid | 24725434900 | |
| dc.authorscopusid | 57197579380 | |
| dc.authorwosid | Sokullu, Radosveta/AAD-8071-2019 | |
| dc.contributor.author | Sokullu, Radosveta | |
| dc.contributor.author | Yildirim, Mete | |
| dc.date.accessioned | 2023-01-12T19:50:54Z | |
| dc.date.available | 2023-01-12T19:50:54Z | |
| dc.date.issued | 2022 | |
| dc.department | N/A/Department | en_US |
| dc.description.abstract | In this study, we focus on realizing channel estimation using a fully connected deep neural network. The data aided estimation approach is employed. We assume the transmission channel is Rayleigh and it is constant over the duration of a symbol plus pilot transmission. We develop and tune the deep learning model for various size of pilot data that is known to the receiver and used for channel estimation. The deep learning models are trained on the Rayleigh channel. The performance of the model is discussed for various size of pilot by providing Bit Error Rate of the model. The Bit Error Rate performance of the model is compared to theoretical upper bound which shows that the model successfully estimates the channel. | en_US |
| dc.identifier.doi | 10.1007/s11277-022-09657-3 | |
| dc.identifier.endpage | 2317 | en_US |
| dc.identifier.issn | 0929-6212 | |
| dc.identifier.issn | 1572-834X | |
| dc.identifier.issn | 0929-6212 | en_US |
| dc.identifier.issn | 1572-834X | en_US |
| dc.identifier.issue | 3 | en_US |
| dc.identifier.scopus | 2-s2.0-85125071556 | en_US |
| dc.identifier.scopusquality | Q2 | en_US |
| dc.identifier.startpage | 2305 | en_US |
| dc.identifier.uri | https://doi.org/10.1007/s11277-022-09657-3 | |
| dc.identifier.uri | https://hdl.handle.net/11454/76191 | |
| dc.identifier.volume | 125 | en_US |
| dc.identifier.wos | WOS:000759393700001 | en_US |
| dc.identifier.wosquality | Q3 | en_US |
| dc.indekslendigikaynak | Web of Science | en_US |
| dc.indekslendigikaynak | Scopus | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Springer | en_US |
| dc.relation.ispartof | Wireless Personal Communications | en_US |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | Channel estimation | en_US |
| dc.subject | Deep learning | en_US |
| dc.subject | Machine learning | en_US |
| dc.subject | Rayleigh channel | en_US |
| dc.title | Channel Estimation with Fully Connected Deep Neural Network | en_US |
| dc.type | Article | en_US |
