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A Multi-wall and Multi-frequency Indoor Path Loss Prediction Model Using Artificial Neural Networks

  • Research Article - Computer Engineering and Computer Science
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Abstract

Indoor radio propagation prediction modeling has been for a long time an important area of interest in research and development. In the literature, many propagation models are classified into empirical and deterministic models. The accuracy of both categories of models can be improved based on model calibration or tuning that uses real measurements collected in a given environment and frequency. Based on the availability of a huge measurement database, we aimed in this paper to develop a new propagation model using artificial neural networks. The new model is inspired from multi-wall one and will be available for the most used system bands, such as GSM, UMTS and WiFi. The model will be a multilayer perceptron and is trained with measured data using a back-propagation learning algorithm. Evaluated model performances show a high improvement in terms of accuracy compared to a calibrated multi-wall model.

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Authors and Affiliations

  1. Higher School of Communication of Tunisia (Sup’Com), El-Ghazela Technoparc, Route de Raoued Km 3,5, 2083, Ariana, Tunisia

    Aymen Ben Zineb & Mohamed Ayadi

Authors
  1. Aymen Ben Zineb
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  2. Mohamed Ayadi
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Correspondence to Aymen Ben Zineb.

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Zineb, A.B., Ayadi, M. A Multi-wall and Multi-frequency Indoor Path Loss Prediction Model Using Artificial Neural Networks. Arab J Sci Eng 41, 987–996 (2016). https://doi.org/10.1007/s13369-015-1949-6

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  • DOI: https://doi.org/10.1007/s13369-015-1949-6

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