Identification of the number of wireless channel taps using deep neural networks
| dc.authorid | 0000-0001-8856-4895 | |
| dc.authorid | 0000-0002-9054-0005 | |
| dc.authorid | 0000-0001-9474-7372 | |
| dc.contributor.author | Jaradat, Ahmad M. | |
| dc.contributor.author | Elgammal, Khaled Walid | |
| dc.contributor.author | Özdemir, Mehmet Kemal | |
| dc.contributor.author | Arslan, Hüseyin | |
| dc.date.accessioned | 2021-09-24T06:45:33Z | |
| dc.date.available | 2021-09-24T06:45:33Z | |
| dc.date.issued | 2021 | |
| dc.department | İstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü | |
| dc.description.abstract | In wireless communication systems, identifying the number of channel taps offers an enhanced estimation of the channel impulse response (CIR). In this work, efficient identification of the number of wireless channel taps has been achieved via deep neural networks (DNNs), where we modified an existing DNN and analyzed its convergence performance using only the transmitted and received signals of a wireless system. The displayed results demonstrate that the adopted DNN accomplishes superior performance in identifying the number of channel taps, as compared to an existing algorithm called Spectrum Weighted Identification of Signal Sources (SWISS). | |
| dc.identifier.citation | Jaradat, A. M., Elgammal, K. W., Özdemir, M. K. ve Arslan, H. (2021). Identification of the number of wireless channel taps using deep neural networks. 19th IEEE International New Circuits and Systems Conference, NEWCAS 2021. Toulon, 13-16 June 2021. https://dx.doi.org/10.1109/NEWCAS50681.2021.9462770 | |
| dc.identifier.doi | 10.1109/NEWCAS50681.2021.9462770 | |
| dc.identifier.isbn | 9781665424295 | |
| dc.identifier.scopusquality | N/A | |
| dc.identifier.uri | https://dx.doi.org/10.1109/NEWCAS50681.2021.9462770 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12511/8289 | |
| dc.identifier.wosquality | N/A | |
| dc.indekslendigikaynak | Web of Science | |
| dc.indekslendigikaynak | Scopus | |
| dc.language.iso | en | |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | |
| dc.relation.ispartof | 19th IEEE International New Circuits and Systems Conference, NEWCAS 2021 | en_US |
| dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.subject | Channel Identification | |
| dc.subject | Channel Impulse Response | |
| dc.subject | Channel Taps | |
| dc.subject | Deep Neural Network | |
| dc.subject | Wireless Channel | |
| dc.title | Identification of the number of wireless channel taps using deep neural networks | |
| dc.type | Conference Object |
