Deep learning-based optimal ris interaction exploiting previously sampled channel correlations
| dc.authorid | 0000-0002-1797-8238 | |
| dc.authorid | 0000-0003-3375-0310 | |
| dc.authorid | 0000-0001-9474-7372 | |
| dc.contributor.author | Aygül, Mehmet Ali | |
| dc.contributor.author | Nazzal, Mahmoud | |
| dc.contributor.author | Arslan, Hüseyin | |
| dc.date.accessioned | 2021-11-04T06:39:15Z | |
| dc.date.available | 2021-11-04T06:39:15Z | |
| dc.date.issued | 2021 | |
| dc.department | İstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü | |
| dc.description.abstract | The reconfigurable intelligent surface (RIS) technology has attracted interest due to its promising coverage and spectral efficiency features. However, some challenges need to be addressed to realize this technology in practice. One of the main challenges is the configuration of reflecting coefficients without the need for beam training overhead or massive channel estimation. Earlier works used estimated channel information with deep learning algorithms to design RIS reflection matrices. Although these works can reduce the beam training overhead, still they overlook existing correlations in the previously sampled channels. In this paper, different from existing works, we propose to exploit the correlation in the previously sampled channels to estimate RIS interaction more reliably. We use a deep multi-layer perceptron for this purpose. Simulation results reveal performance improvements achieved by the proposed algorithm. | |
| dc.identifier.citation | Aygül, M. A., Nazzal, M. ve Arslan, H. (2021). Deep learning-based optimal ris interaction exploiting previously sampled channel correlations. IEEE Wireless Communications and Networking Conference (WCNC). Nanjing, Peoples R China, March 29-April 01, 2021. https://dx.doi.org/10.1109/WCNC49053.2021.9417591 | |
| dc.identifier.doi | 10.1109/WCNC49053.2021.9417591 | |
| dc.identifier.isbn | 9781728195056 | |
| dc.identifier.issn | 1525-3511 | |
| dc.identifier.scopusquality | N/A | |
| dc.identifier.uri | https://dx.doi.org/10.1109/WCNC49053.2021.9417591 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12511/8560 | |
| dc.identifier.wosquality | N/A | |
| dc.indekslendigikaynak | Web of Science | |
| dc.indekslendigikaynak | Scopus | |
| dc.language.iso | en | |
| dc.publisher | IEEE - Institute of Electrical and Electronics Engineers, Inc | |
| dc.relation.ispartof | IEEE Wireless Communications and Networking Conference (WCNC) | en_US |
| dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | |
| dc.relation.tubitak | info:eu-repo/grantAgreement/TUBITAK/SOBAG/119E433 | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.subject | Deep Learning | |
| dc.subject | Massive MIMO | |
| dc.subject | Phase Optimization | |
| dc.subject | Previous Channel Information | |
| dc.subject | Reconfigurable Intelligent Surface | |
| dc.title | Deep learning-based optimal ris interaction exploiting previously sampled channel correlations | |
| dc.type | Conference Object |
