• Emad Ahmed Hussien Electrical Engineering Department, Mustansiriyah University, Baghdad, Iraq https://orcid.org/0000-0003-3089-1302
  • Ghanim Abdulkareem Electrical Engineering Department, Mustansiriyah University, Baghdad, Iraq




block-pilot, comb-pilot, least square, linear minimum mean square error, Rayleigh fading, time domain estimation


With the rapid development of wireless communication, 5G is gradually growing into a large-scale basic Internet that supports various industries in the whole society. The substantial expansion of its service scope poses many challenges for the underlying technology, especially for the crucial component of the physical Layer-Orthogonal Frequency Division Multiplexing (OFDM). Recently, Neural Networks (NNs) have attracted extensive attention due to their excellent performance in computing vision and natural language processing. Its strong universality also provides new development space for traditional communications. This manuscript conducts an in-depth study on channel estimation for OFDM systems and explores the possible application of a Generalized Regression Neural Network (GRNN) to estimate the Channel Impulse Response (CIR) attenuated by AWGN and Rayleigh fading system. Moreover, three traditional channel estimation algorithms, i.e., LS, MMSE, and LMMSE, are derived by mathematics. In addition, this thesis illustrates several typical neural networks in detail, including their internal structure, parameter updating process, and related optimization algorithms.


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Submission Dates

Received 11/12/2022

Accepted 20/1/2023

Published 1/5/2023

How to Cite

Ahmed Hussien, E., & Abdulkareem, G. . (2023). RAYLEIGH FADING CHANNEL ESTIMATION BASED ON GENERALIZED REGRESSION NEURAL NETWORK. Journal of Engineering and Sustainable Development, 27(3), 363–374. https://doi.org/10.31272/jeasd.27.3.6