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- Le, Anh-Tuan (1)
- Luong, Thien Van (1)
- Nguyen, Cuong V. (1)
- Pham, T. Linh (1)
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- Autoencoder (1)
- deep neural network (1)
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We theoretically investigate the plasmonic properties of mid-infrared graphene-based metamaterials and apply deep learning of a neural network for the inverse design. These artificial structures have square periodic arrays of graphene plasmonic resonators deposited on dielectric thin films. Optical spectra vary significantly with changes in structural parameters. To validate our theoretical approach, we carry out finite difference time domain simulations and compare computational results with theoretical calculations. Quantitatively good agreements among theoretical predictions, simulations, and previous experiments allow us to employ this proposed theoretical model to generate reliable data for training and testing deep neural networks. By merging the pre-trained neural network wit... |
This paper proposes a deep learning (DL)-aided multicarrier (MC) system operating on fading channels, where both modulation and demodulation blocks are modeled by deep neural networks (DNNs), regarded as the encoder and decoder of an autoencoder (AE) architecture, respectively. Unlike existing AE-based systems, which incorporate domain knowledge of a channel equalizer to suppress the effects of wireless channels, the proposed scheme, termed as MC-AE, directly feeds the decoder with the channel state information and received signal, which are then processed in a fully data-driven manner. This new approach enables MC-AE to jointly learn the encoder and decoder to optimize the diversity and coding gains over fading channels. In particular, the block error rate of MC-AE is analyzed to s... |
