CNN-Based Signal Detector for IM-OFDMA
| dc.contributor.author | Alaca, Ozgur | |
| dc.contributor.author | Althunibat, Saud | |
| dc.contributor.author | Yarkan, Serhan | |
| dc.contributor.author | Miller, Scott L. | |
| dc.contributor.author | Qaraqe, Khalid A. | |
| dc.date.accessioned | 2023-01-18T12:41:48Z | |
| dc.date.available | 2023-01-18T12:41:48Z | |
| dc.date.issued | 2022 | en_US |
| dc.department | Fakülteler, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü | en_US |
| dc.description.abstract | The recently proposed index modulation-based up-link orthogonal frequency division multiple access (IM-OFDMA) scheme has outperformed the conventional schemes in terms of spectral efficiency and error performance. However, the induced computational complexity at the receiver forms a bottleneck in real-time implementation due to the joint detection of all users. In this paper, based on deep learning principles, a convolutional neural network (CNN)-based signal detector is proposed for data detection in IM-OFDMA systems instead of the optimum Maximum Likelihood (ML) detector. A CNN-based detector is constructed with the created dataset of the IM-OFDMA transmission by offline training. Then, the convolutional neural network (CNN)-based detector is directly applied to the IM-OFMDA communication scheme to detect the transmitted signal by treating the received signal and channel state information (CSI) as inputs. The proposed CNN-based detector is able to reduce the order of the computational complexity from O(n2n) to O(n2) as compared to the ML detector with a slight impact on the error performance. | en_US |
| dc.identifier.doi | 10.1109/GLOBECOM46510.2021.9685285 | en_US |
| dc.identifier.scopus | 2-s2.0-85184374266 | en_US |
| dc.identifier.scopusquality | N/A | en_US |
| dc.identifier.uri | https://hdl.handle.net/11467/6069 | |
| dc.identifier.uri | https://doi.org/10.1109/GLOBECOM46510.2021.9685285 | |
| dc.identifier.wos | WOS:000790747201070 | en_US |
| dc.identifier.wosquality | N/A | en_US |
| dc.indekslendigikaynak | Web of Science | en_US |
| dc.indekslendigikaynak | Scopus | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
| dc.relation.ispartof | 2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings | en_US |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/embargoedAccess | en_US |
| dc.subject | convolutional neural net-works; index modulation; Multiple access; orthogonal frequency division multiple access; signal detection | en_US |
| dc.title | CNN-Based Signal Detector for IM-OFDMA | en_US |
| dc.type | Article | en_US |
