Low-complexity deep learning-based beamforming in MISO systems

Moe Thet N. W., Elgammal K. W. M. A., Ates H. F., ÖZDEMİR M. K.

29th IEEE Conference on Signal Processing and Communications Applications, SIU 2021, Virtual, Istanbul, Türkiye, 9 - 11 Haziran 2021 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/siu53274.2021.9478043
  • Basıldığı Şehir: Virtual, Istanbul
  • Basıldığı Ülke: Türkiye
  • Anahtar Kelimeler: Deep learning (DL), beamforming neural network (BFNN), convolutional neural network (CNN), multiple-input single-output (MISO), millimeter wave (mmWave)
  • İstanbul Medipol Üniversitesi Adresli: Evet

Özet

This study proposes a low-complexity deep learning-based beamforming neural network (BFNN) for massive multiple-input single-output (MISO) systems. We adopt an unsupervised learning-based convolutional neural network (CNN) model. The network is trained to obtain an analog phase shifters (PSs)-based beamforming vector of a given user by maximizing the system spectral efficiency (SE) while maintaining the transmitted power constraint. The channel state information (CSI) for millimeter wave (mmWave) channel and signal-to-noise-ratio (SNR) are used as inputs to the network. We also proposed a novel input feeding arrangement to the network and assessed its performance by using different input data representations. Simulation results show that the CNN-BFNN has the lowest complexity compared to a fully connected neural network (FCNN) and the existing conventional algorithms. Furthermore, the CNN model with fast Fourier transform (FFT) input provides the highest SE performance among all other input data representations.