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Estimation of $$\alpha -\kappa -\mu $$ α - κ - μ mobile fading channel parameters using evolutionary algorithms

Author

Listed:
  • Carlos Paula Lemos

    (Ciência e Tecnologia do Triângulo Mineiro)

  • Antônio Cláudio Paschoarelli Veiga

    (Universidade Federal de Uberlândia)

  • Sandro Adriano Fasolo

    (Universidade Federal de São João del Rei)

Abstract

This paper proposes the use of evolutionary algorithms (EAs) to estimate the physical parameters of the generalized $$\alpha -\kappa -\mu $$ α - κ - μ mobile fading channel model. The estimation of parameters is a fundamental step that allows for the statistical model to adjust to the real experimental data. The The maximum likelihood estimation (MLE) method that is traditionally used for estimating parameters of the $$\alpha -\kappa -\mu $$ α - κ - μ channel uses nonlinear numerical methods. In some cases, the use of nonlinear numerical methods may lead the MLE to make physically unacceptable estimations, or even to not be able to obtain a result. Our proposal is to innovate the existing EAs by incorporating an adaptive approach, a new mutation strategy and an adequate fitness function for the estimation of $$\alpha -\kappa -\mu $$ α - κ - μ parameters. Experimental results are presented to confirm that parameters estimated by the EAs (genetic algorithms, differential evolution algorithms, and differential evolution algorithms with an adaptive guiding mechanism) are all physically acceptable. These experiments show that the EAs outperform MLE estimation results.

Suggested Citation

  • Carlos Paula Lemos & Antônio Cláudio Paschoarelli Veiga & Sandro Adriano Fasolo, 2021. "Estimation of $$\alpha -\kappa -\mu $$ α - κ - μ mobile fading channel parameters using evolutionary algorithms," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 77(1), pages 189-211, May.
    • Handle: RePEc:spr:telsys:v:77:y:2021:i:1:d:10.1007_s11235-020-00743-0
    DOI: 10.1007/s11235-020-00743-0
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    References listed on IDEAS

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    1. Gilberto Fernandes & Joel J. P. C. Rodrigues & Luiz Fernando Carvalho & Jalal F. Al-Muhtadi & Mario Lemes Proença, 2019. "A comprehensive survey on network anomaly detection," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 70(3), pages 447-489, March.
    2. Dhuha Abdulhadi Abduljabbar & Siti Zaiton Mohd Hashim & Roselina Sallehuddin, 2020. "Nature-inspired optimization algorithms for community detection in complex networks: a review and future trends," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 74(2), pages 225-252, June.
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