CNN-Based Signal Detector for IM-OFDMA
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Date
2022
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers Inc.
Access Rights
info:eu-repo/semantics/embargoedAccess
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.
Description
Keywords
convolutional neural net-works; index modulation; Multiple access; orthogonal frequency division multiple access; signal detection
Journal or Series
2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings
WoS Q Value
N/A
Scopus Q Value
N/A
