Deep Learning-Based Channel Estimation for OFDM Systems under Weibull Fading Channel Conditions

Adıgüzel Ö., Develi İ.

3rd International Conference on Engineering and Applied Natural Sciences, Konya, Turkey, 14 - 17 January 2023, pp.399-404

  • Publication Type: Conference Paper / Full Text
  • City: Konya
  • Country: Turkey
  • Page Numbers: pp.399-404
  • Erciyes University Affiliated: Yes


Orthogonal frequency-division multiplexing (OFDM) is commonly used in wireless communication systems due to its high performance over frequency selective fading channels and also the effect on reducing the inter-symbol interference (ISI). This paper investigates the performance of deep learning-based channel estimation for OFDM systems over Weibull fading channels. In wireless communication systems, the transmitted signal is exposed to various obstacles and fading occurs. Channel state information (CSI) is needed to correct the errors of the transmitted signal. To obtain the CSI, channel estimation methods such as least square (LS) and minimum mean square error (MMSE) are widely used. Deep learning-based channel estimation can have a higher symbol error rate (SER) performance than LS and MMSE methods. In addition, classical channel estimation methods such as the MMSE method have complexity. Deep learning-based channel estimation can reduce the complexity of estimation methods. In this study, the performances of the proposed method and classic channel estimation methods in different channel conditions were compared by using computer simulations. It has been observed that deep learning-based channel estimation has higher performance than classic methods in different channel conditions with its ability to learn and adapt. Simulation results show that deep learning is a promising and high potential tool for channel estimation in OFDM systems.