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Fast-Fading Channel Environment Estimation Using Linear Minimum Mean Squares Error-Support Vector Regression

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Abstract

In this article, we propose a Linear Minimum Mean Squares Error-Support Vector Machine regression (LMMSE-SVR) approach which is applied to Long Term Evolution (LTE) downlink channel environment estimation under high mobility conditions. LMMSE-SVR is employed to track and estimate the rapid variations of a realistic Extended Vehicular A model channel according to 3GPP specifications. This contribution assimilates both channel estimation at reference signals and interpolation at data signals into the LMMSE-SVR method. Performances of our channel environment estimation proposal in terms of Bit Error Rate and Mean Squares Error are established via simulation for both normal and extended Cyclic Prefix scenarios in LTE downlink system with 64-QAM modulation scheme under 350 km/h mobile speed.

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Authors and Affiliations

  1. Tunisian Military Research Center, 2045, Laouina, Tunisia

    Anis Charrada Ph.D

  2. SERCOM, Tunisia Polytechnic School, University of Carthage, Box 743, 2078, La Marsa, Tunisia

    Anis Charrada Ph.D

  3. INRS, EMT Center, 800, de la Gauchetiére W., Suite 6900, Montreal, QC, H5A 1K6, Canada

    Abdelaziz Samet

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  1. Anis Charrada Ph.D
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  2. Abdelaziz Samet
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Correspondence to Anis Charrada Ph.D.

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Charrada, A., Samet, A. Fast-Fading Channel Environment Estimation Using Linear Minimum Mean Squares Error-Support Vector Regression. Wireless Pers Commun 106, 1897–1913 (2019). https://doi.org/10.1007/s11277-018-5728-z

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  • DOI: https://doi.org/10.1007/s11277-018-5728-z

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