Reconfigurable Intelligent Surface-assisted Classification of Modulations using Deep Learning

• URL: http://arxiv.org/abs/2209.08388v1
• Date: Sat, 17 Sep 2022 18:45:03 GMT
• Title: Reconfigurable Intelligent Surface-assisted Classification of Modulations using Deep Learning
• Authors: Mir Lodro, Hamidreza Taghvaee, Jean Baptiste Gros, Steve Greedy, Geofrroy Lerosey, and Gabriele Gradoni
• Abstract summary: We present a RIS-assisted digital classification method based on artificial intelligence. We train a convolutional neural network to classify digital modulations. The accuracy of the proposed classification method is found to be remarkable, particularly for low levels of SNR.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The fifth generating (5G) of wireless networks will be more adaptive and heterogeneous. Reconfigurable intelligent surface technology enables the 5G to work on multistrand waveforms. However, in such a dynamic network, the identification of specific modulation types is of paramount importance. We present a RIS-assisted digital classification method based on artificial intelligence. We train a convolutional neural network to classify digital modulations. The proposed method operates and learns features directly on the received signal without feature extraction. The features learned by the convolutional neural network are presented and analyzed. Furthermore, the robust features of the received signals at a specific SNR range are studied. The accuracy of the proposed classification method is found to be remarkable, particularly for low levels of SNR.

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