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Robust and Sparse Dual Tree Complex Wavelet Transform-Based Twin Support Vector Regression for 5G InH and V2I Communications

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Abstract

In this article, a robust and sparse Twin Support Vector Regression based on Dual Tree Complex Discrete Wavelet Transform algorithm is proposed and applied to dense 5G InH (Indoor Hotspot) LOS (Line-of-Sight) multipath communications for several frequencies 28, 38, 60 and 73-GHz. Moreover, PDF (Probability Distribution Function) and large-scale propagation parameters are determined in terms of free space path loss value (FSPL), standard deviation of Shadow Factor (SF) and PLE (Path Loss Exponent) for each dense InH scenario under consideration according to the Close-In (CI) free space reference distance path loss model. Furthermore, large-scale analysis for 5G outdoor Vehicular-to-Infrastructure (V2I) NLOS communications are investigated in terms of measured path loss values (FSPL, PLE, SF, PDF) for mmWave frequencies 28, 38, 60 and 73 GHz. Additionally, the outdoor V2I communications scenarios based on two types of horn antennas (22 deg/15 dBi and 07 deg/25 dBi) and a constantly aligning mechanism between Tx and Rx antenna beams are considered and evaluated.

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Authors and Affiliations

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

    Anis Charrada

  2. Science and Technology for Defense Lab., CRM, 2045, Aouina, Tunisia

    Anis Charrada

  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
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Charrada, A., Samet, A. Robust and Sparse Dual Tree Complex Wavelet Transform-Based Twin Support Vector Regression for 5G InH and V2I Communications. Wireless Pers Commun 128, 1603–1630 (2023). https://doi.org/10.1007/s11277-022-10011-w

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