mm-wave chipless RFID decoding: introducing image-based deep learning techniques

Larry M. Arjomandi, Grishma Khadka, Nemai C. Karmakar

Research output: Contribution to journalArticleResearchpeer-review


Chipless RFID tag decoding has some inherent degrees of uncertainty because there is no handshake protocol between chipless tags and readers. This paper initially compares the outcome of different pattern recognition methods to decode some frequency-based tags in the mm-wave spectrum. It will be shown that these pattern recognition methods suffer from almost 2 to 5% false decoding rate. To overcome this mis-decoding problem, two novel methods of making images of the chipless tags are presented. The first method is making 2-D images based on side looking aperture radar concepts, and the second one is making virtual 2-D images from 1-D backscattering signals. Then a 2-D decoding algorithm is suggested based on a convolutional neural network to decode those tag images and compare the results. It is shown that this combined decoding method has very high accuracy, and it almost eliminates any ambiguity and false decoding problems. This is the first time a deep-learning method is used with image-construction methods to decode chipless RFID tags.

Original languageEnglish
Pages (from-to)3700-3709
Number of pages10
JournalIEEE Transactions on Antennas and Propagation
Issue number5
Publication statusPublished - May 2022


  • chipless RFID tags
  • chipless sensors
  • convolutional neural network
  • Decision trees
  • Decoding
  • Deep-learning
  • mm-wave band
  • Pattern recognition
  • pattern recognition
  • Radar imaging
  • RFID
  • RFID tags
  • Side Looking Aperture Radar
  • Substrates
  • Synthetic Aperture Radar
  • Training

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