Dictionary Learning-Based Beamspace Channel Estimation in Millimeter-Wave Massive MIMO Systems with a Lens Antenna Array

Nazzal M., Aygul M. A., GÖRÇİN A., Arslan H.

15th IEEE International Wireless Communications and Mobile Computing Conference (IEEE IWCMC), Tangier, Fas, 24 - 28 Haziran 2019, ss.20-25 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/iwcmc.2019.8766499
  • Basıldığı Şehir: Tangier
  • Basıldığı Ülke: Fas
  • Sayfa Sayıları: ss.20-25
  • Anahtar Kelimeler: Beamspace channel estimation, beam selection, millimeter-waves, massive MIMO, sparse coding, dictionary learning
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

Recent research considers the application of a lens antenna array in order to provide efficient beam selection in beamspace massive MIMO. Achieving the advantages of this beam selection paradigm requires efficient channel estimation in the beamspace. Along this line, beamspace sparsity is an efficient regularizer to this problem. In this paper, we propose using a dictionary trained over a set of example beam selection matrices, as a beam selection tool. In this context, a learned dictionary can more effectively guarantee the sparsity of the representation at the specified sparsity level, owing to the dictionary learning process. This means that it gives a better sparse representation, and, consequently, a better channel estimation quality. Simulations validate that using a trained dictionary improves the quality of channel estimation, as tested over two channel models with different operating scenarios.