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Fading channel modelling using single-hidden layer feedforward neural networks

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Abstract

Due to the complexity and extensive application of wireless systems, fading channel modeling is of great importance for designing a mobile network, especially for high speed environments. High mobility challenges the speed of channel estimation and model optimization. In this study, we propose a single-hidden layer feedforward neural network (SLFN) approach to modelling fading channels, including large-scale attenuation and small-scale variation. The arrangements of SLFN in path loss (PL) prediction and fading channel estimation are provided, and the information in both of them is trained with extreme learning machine (ELM) algorithm and a faster back-propagation (BP) algorithm called Levenberg-Marquardt algorithm. Computer simulations show that our proposed SLFN estimators could obtain PL prediction and the instantaneous channel transfer function of sufficient accuracy. Furthermore, compared with BP algorithm, the ability of ELM to provide millisecond-level learning makes it very suitable for fading channel modelling in high speed scenarios.

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Acknowledgments

This work was supported by the Natural Science Foundation of Zhejiang province (China under Grants LY15F030017, LY12F03017 and LQ13G010005), National Natural Science Foundation of China (China under Grants 61403224).

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Authors and Affiliations

  1. Institution of Information Science and Electrical Engineering, Zhejiang University, Hangzhou, 310037, Zhejiang, China

    Junbiao Liu & Xinyu Jin

  2. School of Information and Electrical Engineering, Zhejiang University City College, Hangzhou, 310015, Zhejiang, China

    Fang Dong & Hong Liu

  3. Department of Electronic Engineer, Tsinghua University, Beijing, 100084, Beijing, China

    Liang He

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  1. Junbiao Liu
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  2. Xinyu Jin
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  3. Fang Dong
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Correspondence to Junbiao Liu.

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Liu, J., Jin, X., Dong, F. et al. Fading channel modelling using single-hidden layer feedforward neural networks. Multidim Syst Sign Process 28, 885–903 (2017). https://doi.org/10.1007/s11045-015-0380-1

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