RAYLEIGH FADING CHANNEL ESTIMATION BASED ON GENERALIZED REGRESSION NEURAL NETWORK

Authors

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

DOI:

https://doi.org/10.31272/jeasd.27.3.6

Keywords:

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

Abstract

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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Key Dates

Published

2023-05-01

Issue

Vol. 27 No. 3 (2023): Journal of Engineering and Sustainable Development

Section

Articles

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Copyright (c) 2023 Emad Ahmed Hussien, Ghanim Abdulkareem

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How to Cite

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

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