An Improved Scheme for Deep Learning Channel Estimation

Han T., Zhang Y., Lv X., Temiz M.

4th International Symposium on Computer Technology and Information Science, ISCTIS 2024, Xian, Çin, 12 - 14 Temmuz 2024, ss.13-17, (Tam Metin Bildiri) identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/isctis63324.2024.10699086
  • Basıldığı Şehir: Xian
  • Basıldığı Ülke: Çin
  • Sayfa Sayıları: ss.13-17
  • Anahtar Kelimeler: Channel estimation, deep learning, DFT, DNN, OFDM
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

For the channel estimation problem in the Orthogonal Frequency Division Multiplexing (OFDM) system, we propose an optimization scheme based on the existing deep neural network (DNN) channel estimation. In this scheme, the Discrete Fourier Transform (DFT) is used to process the input data to eliminate the influence of noise in the Least Squares (LS) method, so as to improve the learning ability of the DNN model. Through simulation experiments, it can be seen that the proposed scheme can effectively improve the performance of DNN channel estimation.