Reconfigurable Intelligent Surface-assisted Classification of Modulations using Deep Learning

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

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 ac-curacy of the proposed classification method is found to be remarkable, particularly for low levels of SNR.
Original languageEnglish
Title of host publication2022 3rd URSI Atlantic and Asia Pacific Radio Science Meeting (AT-AP-RASC)
Place of PublicationGran Canaria, Spain
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISBN (Electronic)978-9-4639-6-8058
ISBN (Print)978-1-6654-9986-6
DOIs
Publication statusPublished - 6 Jul 2022

Keywords

  • Reconfigurable intelligent Surfaces
  • Deep Learning for Communications
  • Software Defined Radio
  • PHY Layer Security
  • Signal Classification
  • Indoor Environment
  • Prototype Testing

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