CNN-Based Signal Detector for IM-OFDMA

Ozgur Alaca, Saud Althunibat, Serhan Yarkan, Scott L. Miller, Khalid A. Qaraqe

Research output: Contribution to journalConference articlepeer-review

15 Scopus citations

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.

Original languageEnglish
JournalProceedings - IEEE Global Communications Conference, GLOBECOM
DOIs
StatePublished - 2021
Externally publishedYes
Event2021 IEEE Global Communications Conference, GLOBECOM 2021 - Madrid, Spain
Duration: Dec 7 2021Dec 11 2021

Funding

This publication was made possible by NPRP12S-0225-190152 from the Qatar National Research Fund (a member of The Qatar Foundation). The statements made herein are solely the responsibility of the authors.

Keywords

  • Multiple access
  • convolutional neural net-works
  • index modulation
  • orthogonal frequency division multiple access
  • signal detection

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