Luong, Thien Van, Ko, Youngwook, Vien, Ngo Anh et al. (2 more authors) (2019) Deep Learning-Based Detector for OFDM-IM. IEEE wireless communications letters. pp. 1159-1162. ISSN 2162-2345
Abstract
This letter presents the first attempt of exploiting deep learning (DL) in the signal detection of orthogonal frequency division multiplexing with index modulation (OFDM-IM) systems. Particularly, we propose a novel DL-based detector termed as DeepIM, which employs a deep neural network with fully connected layers to recover data bits in an OFDM-IM system. To enhance the performance of DeepIM, the received signal and channel vectors are pre-processed based on the domain knowledge before entering the network. Using datasets collected by simulations, DeepIM is first trained offline to minimize the bit error rate (BER) and then the trained model is deployed for the online signal detection of OFDM-IM. Simulation results show that DeepIM can achieve a near-optimal BER with a lower runtime than existing hand-crafted detectors.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Electronic Engineering (York) The University of York |
Depositing User: | Pure (York) |
Date Deposited: | 08 Oct 2019 09:40 |
Last Modified: | 02 Apr 2025 23:17 |
Published Version: | https://doi.org/10.1109/LWC.2019.2909893 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1109/LWC.2019.2909893 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:151899 |