| dc.contributor.author | Seyman, M. Nuri | |
| dc.contributor.author | Taspinar, Necmi | |
| dc.date.accessioned | 2020-06-25T18:07:07Z | |
| dc.date.available | 2020-06-25T18:07:07Z | |
| dc.date.issued | 2013 | |
| dc.identifier.citation | closedAccess | en_US |
| dc.identifier.issn | 2193-567X | |
| dc.identifier.issn | 2191-4281 | |
| dc.identifier.uri | https://doi.org/10.1007/s13369-013-0586-1 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12587/5483 | |
| dc.description | WOS: 000322114200022 | en_US |
| dc.description.abstract | Orthogonal frequency division multiplexing (OFDM) combined with multiple input multiple output (MIMO) antennas is one of the promising schemes for high rate data transmission and capacity improvement. However, in these systems, channel estimation task is critical for coherent detection and demodulation. In this study, we have proposed a channel estimator based on radial basis function neural network trained by gradient descent method for MIMO-OFDM systems. Simulation results show that the proposed estimator performs better than other considered channel estimation techniques. | en_US |
| dc.language.iso | eng | en_US |
| dc.publisher | Springer Heidelberg | en_US |
| dc.relation.isversionof | 10.1007/s13369-013-0586-1 | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | MIMO-OFDM | en_US |
| dc.subject | Channel estimation | en_US |
| dc.subject | Radial basis function neural network | en_US |
| dc.title | Radial Basis Function Neural Networks for Channel Estimation in MIMO-OFDM Systems | en_US |
| dc.type | article | en_US |
| dc.contributor.department | Kırıkkale Üniversitesi | en_US |
| dc.identifier.volume | 38 | en_US |
| dc.identifier.issue | 8 | en_US |
| dc.identifier.startpage | 2173 | en_US |
| dc.identifier.endpage | 2178 | en_US |
| dc.relation.journal | Arabian Journal For Science And Engineering | en_US |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
