Alaca, OzgurAlthunibat, SaudYarkan, SerhanMiller, Scott L.Qaraqe, Khalid A.2023-01-182023-01-182022https://hdl.handle.net/11467/6069https://doi.org/10.1109/GLOBECOM46510.2021.9685285The 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.eninfo:eu-repo/semantics/embargoedAccessconvolutional neural net-works; index modulation; Multiple access; orthogonal frequency division multiple access; signal detectionArticleN/AWOS:000790747201070N/A2-s2.0-8518437426610.1109/GLOBECOM46510.2021.9685285