Convolutional Neural Network-Based Signal Classification in Real Time


Cetin R., Gecgel S., Karabulut Kurt G. Z., Baskaya F.

IEEE EMBEDDED SYSTEMS LETTERS, cilt.13, sa.4, ss.186-189, 2021 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 13 Sayı: 4
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1109/les.2021.3049731
  • Dergi Adı: IEEE EMBEDDED SYSTEMS LETTERS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC
  • Sayfa Sayıları: ss.186-189
  • Anahtar Kelimeler: Bartlett method, convolutional neural network (CNN), deep learning, signal classification, software defined radio, COGNITIVE RADIO
  • İstanbul Teknik Üniversitesi Adresli: Evet

Özet

The capability to discriminate signals in the wireless spectrum is significantly crucial in a wide range of practices, such as mesh networks, military, and defense applications. Motivated by these applications, we propose a signal classification scheme deployed in embedded software-defined radio. The proposed work is differentiated by designing the system for over-the-air signals and considering real-life conditions: hardware-constraints and varying channel congestions. The modified Bartlett-based method is unified with a lightweight CNN on a standalone embedded device. Datasets are produced by over-the-air measurements from a WLAN modem that reflects the sparse and dense channel scenarios. The influence of bit resolution and dimension count of the dataset on classification is monitored. We demonstrate the designed system performance across a variety of traffic rates to examine stability. Additionally, several state-of-the-art CNNs are compared with the proposed CNN for accuracy, model complexity, and FLOPS. Our CNN architecture demonstrates immensely nearby accuracy (0.7% - 2.1%) to the rivals despite its exceedingly lightweight architecture.