dc.contributor.author | Aygül, Mehmet Ali | |
dc.contributor.author | Çırpan, Hakan Ali | |
dc.contributor.author | Arslan, Hüseyin | |
dc.date.accessioned | 2024-06-12T07:17:06Z | |
dc.date.available | 2024-06-12T07:17:06Z | |
dc.date.issued | 2023 | en_US |
dc.identifier.citation | Aygül, M. A., Çırpan, H. A. ve Arslan, H. (2023). Deep multi-task learning-based simultaneous channel tap and coefficient estimation. 6th IEEE Future Networks World Forum, FNWF 2023, Baltimore, November 13-15, 2023. http://dx.doi.org/10.1109/FNWF58287.2023.10520577 | en_US |
dc.identifier.isbn | 9798350324587 | |
dc.identifier.uri | http://dx.doi.org/10.1109/FNWF58287.2023.10520577 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12511/12627 | |
dc.description.abstract | Wireless communication systems depend on accurate channel estimation to ensure efficient and reliable data transmission. The channel estimation process consists of two essential steps: channel tap and coefficient estimation. Physical layer features such as time arrival, and signal strengths are well used for the tap estimation. However, prior knowledge is required to use these methods. Recently, machine learning-based methods have been proposed. In particular, deep learning (DL)-based methods are promising because they can learn from raw data without much preprocessing, scale well with extensive and diverse datasets, and capture complex relationships. However, these methods overlook the relationship between the channel taps and coefficients. In this paper, we propose a DL-based multi-task learning method to estimate channel taps and coefficients simultaneously. Simulation results reveal that the performance of the proposed tap estimation method is superior to the traditional DL-based tap estimation. Furthermore, the proposed method removes the need to train two models to estimate channel taps and coefficients. | en_US |
dc.description.sponsorship | IEEE Antennas and Propagation Society (APS). IEEE Circuits and Systems Society (CAS). IEEE Communications Society (ComSoc). IEEE Electronics Packaging Society (EPS). IEEE Intelligent Transportation Systems Society (ITSS). | en_US |
dc.language.iso | eng | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | Channel Coefficients | en_US |
dc.subject | Channel Tap Estimation | en_US |
dc.subject | Deep Learning | en_US |
dc.subject | Multi-Task Learning | en_US |
dc.subject | Wireless Channel | en_US |
dc.title | Deep multi-task learning-based simultaneous channel tap and coefficient estimation | en_US |
dc.type | conferenceObject | en_US |
dc.relation.ispartof | 6th IEEE Future Networks World Forum, FNWF 2023 | en_US |
dc.department | İstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü | en_US |
dc.authorid | 0000-0001-9474-7372 | en_US |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
dc.identifier.doi | 10.1109/FNWF58287.2023.10520577 | en_US |
dc.institutionauthor | Arslan, Hüseyin | |
dc.identifier.scopus | 2-s2.0-85194133810 | en_US |