Deep Receiver Design for Multi-carrier Waveforms Using CNNs

YILDIRIM Y., Ozer S., Çırpan H. A.

43rd International Conference on Telecommunications and Signal Processing (TSP), ELECTR NETWORK, 7 - 09 Temmuz 2020, ss.31-36 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/tsp49548.2020.9163562
  • Basıldığı Ülke: ELECTR NETWORK
  • Sayfa Sayıları: ss.31-36
  • Anahtar Kelimeler: CNN, Deep Learning, Deep Receiver Design, GFDM, Multi-carrier Wave-forms, OFDM
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

In this paper, a deep learning based receiver is proposed for a collection of multi-carrier wave-forms including both current and next-generation wireless communication systems. In particular, we propose to use a convolutional neural network (CNN) for jointly detection and demodulation of the received signal at the receiver in wireless environments. We compare our proposed architecture to the classical methods and demonstrate that our proposed CNN-based architecture can perform better on different multi-carrier forms including OFDM and GFDM in various simulations. Furthermore, we compare the total number of required parameters for each network for memory requirements.