Deep Learning-Based Detector for OFDM-IM

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 c...

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Bibliographic Details
Published in:IEEE wireless communications letters 2019-08, Vol.8 (4), p.1159-1162
Main Authors: Luong, Thien Van, Ko, Youngwook, Vien, Ngo Anh, Nguyen, Duy H. N., Matthaiou, Michail
Format: Article
Language:English
Subjects:
Artificial neural networks
Bit error rate
Complexity theory
Computer simulation
Deep learning
deep neural network
DeepIM
Detectors
index modulation
Indexes
Knowledge management
low-complexity detector
Machine learning
OFDM
OFDM-IM
Orthogonal Frequency Division Multiplexing
Signal detection
Signal processing
Simulation
Training
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Summary: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.
ISSN:2162-2337
2162-2345
DOI:10.1109/LWC.2019.2909893