Exploring deep learning for adaptive energy detection threshold determination: A multistage approach
| dc.authorid | 0000-0003-2871-0437 | |
| dc.authorid | 0000-0002-9054-0005 | |
| dc.contributor.author | Bedir, Oğuz | |
| dc.contributor.author | Ekti, Ali Rıza | |
| dc.contributor.author | Özdemir, Mehmet Kemal | |
| dc.date.accessioned | 2023-10-23T09:00:06Z | |
| dc.date.available | 2023-10-23T09:00:06Z | |
| dc.date.issued | 2023 | |
| dc.department | İstanbul Medipol Üniversitesi, Fen Bilimleri Enstitüsü, Elektrik ve Elektronik Mühendisliği ve Siber Sistemler Ana Bilim Dalı | |
| dc.department | İstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü | |
| dc.description.abstract | The concept of spectrum sensing has emerged as a fundamental solution to address the growing demand for accessing the limited resources of wireless communications networks. This paper introduces a straightforward yet efficient approach that incorporates multiple stages that are based on deep learning (DL) techniques to mitigate Radio Frequency (RF) impairments and estimate the transmitted signal using the time domain representation of received signal samples. The proposed method involves calculating the energies of the estimated transmitted signal samples and received signal samples and estimating the energy of the noise using these estimates. Subsequently, the received signal energy and the estimated noise energy, adjusted by a correction factor (k), are employed in binary hypothesis testing to determine the occupancy of the wireless channel under investigation. The proposed system demonstrates encouraging outcomes by effectively mitigating RF impairments, such as carrier frequency offset (CFO), phase offset, and additive white Gaussian noise (AWGN), to a considerable degree. As a result, it enables accurate estimation of the transmitted signal from the received signal, with 3.85% false alarm and 3.06% missed detection rates, underscoring the system’s capability to adaptively determine a decision threshold for energy detection. | |
| dc.description.sponsorship | European Union’s H2020 Framework Programme | en_US |
| dc.identifier.citation | Bedir, O., Ekti, A. R. ve Özdemir, M. K. (2023). Exploring deep learning for adaptive energy detection threshold determination: A multistage approach. Electronics (Switzerland), 12(19). https://doi.org/10.3390/electronics12194183 | |
| dc.identifier.doi | 10.3390/electronics12194183 | |
| dc.identifier.issn | 2079-9292 | |
| dc.identifier.issue | 19 | |
| dc.identifier.scopus | 2-s2.0-85173807086 | |
| dc.identifier.scopusquality | N/A | |
| dc.identifier.uri | https://doi.org/10.3390/electronics12194183 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12511/11623 | |
| dc.identifier.volume | 12 | |
| dc.identifier.wos | 001082977700001 | en_US |
| dc.identifier.wosquality | Q2 | |
| dc.indekslendigikaynak | Web of Science | |
| dc.indekslendigikaynak | Scopus | |
| dc.institutionauthor | Bedir, Oğuz | |
| dc.institutionauthor | Özdemir, Mehmet Kemal | |
| dc.language.iso | en | |
| dc.publisher | Multidisciplinary Digital Publishing Institute (MDPI) | |
| dc.relation.ispartof | Electronics (Switzerland) | en_US |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | |
| dc.relation.tubitak | info:eu-repo/grantAgreement/TUBITAK/SOBAG/121N350 | |
| dc.rights | Attribution 4.0 International | * |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | * |
| dc.subject | Spectrum Sensing | |
| dc.subject | Energy Detection | |
| dc.subject | Deep Learning | |
| dc.subject | Adaptive Threshold | |
| dc.title | Exploring deep learning for adaptive energy detection threshold determination: A multistage approach | |
| dc.type | Article |
