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Investigating Geometry-Aware Network-Based Positioning in Cellular Networks Using Neural Network Predictive Model

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Abstract

Received signal strength (RSS) based positioning techniques are one of the easy and cost effective methods of estimating the location of a mobile station with no Global Positioning System. The major challenge with received signal strength based positioning is high variability in its accuracy due to varying influences in its relationship with parameters such as wireless environment, geometry of the network, propagation model, change in infrastructure and so on. This paper addresses the influence geometry of base stations where location estimation measurements are obtained has on the accuracy of three received signal strength based geometric techniques. A neural network (NN) model for the geometry-aware cellular network was developed using input–output dataset obtained from the system’s model. Levenberg–Marquardt algorithm was employed in training the NN model developed. Results obtained indicated that the geometry of the cellular network used for the network-based positioning had significant impact on the accuracy of the location estimation. Comparison of the influence of network geometry between RSS-based Centre of Gravity, Circular Trilateration and Least Square algorithms using the neural network model were discussed and a critical analysis of the result presented.

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Authors and Affiliations

  1. School of Electrical and Information Engineering, University of Witwatersrand, Private Bag 3, Wits, Johannesburg, 2050, South Africa

    Folasade Mojisola Dahunsi & Barry Dwolatzky

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  1. Folasade Mojisola Dahunsi
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  2. Barry Dwolatzky
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Correspondence to Folasade Mojisola Dahunsi.

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Dahunsi, F.M., Dwolatzky, B. Investigating Geometry-Aware Network-Based Positioning in Cellular Networks Using Neural Network Predictive Model. Wireless Pers Commun 90, 1413–1432 (2016). https://doi.org/10.1007/s11277-016-3401-y

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