Compressed Representation of High Dimensional Channels using Deep Generative Networks

Doshi A., Balevi E., Andrews J. G.

21st IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2020, Georgia, Amerika Birleşik Devletleri, 26 - 29 Mayıs 2020, cilt.2020-May identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 2020-May
  • Doi Numarası: 10.1109/spawc48557.2020.9154297
  • Basıldığı Şehir: Georgia
  • Basıldığı Ülke: Amerika Birleşik Devletleri
  • Anahtar Kelimeler: Channel estimation, CS, GAN, MIMO
  • Orta Doğu Teknik Üniversitesi Adresli: Hayır

Özet

© 2020 IEEE.This paper proposes a novel compressed representation for high dimensional channel matrices obtained by optimization of the input to a deep generative network. Channel estimation using generative networks constrains the reconstructed channel to lie in the range of the generative model, which allows it to outperform conventional channel estimation techniques in the presence of limited number of pilots. It also eliminates the need for explicit knowledge of the sparsifying basis for mmWave multiple-input multiple-output (MIMO) channel matrices, such as the DFT basis, and the associated compressed sensing based strategies for optimal choice of training precoders and combiners. Our approach significantly outperforms sparse signal recovery methods that employ Basis Pursuit Denoising(BPDN) algorithms for narrowband mmWave channel reconstruction.