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Keywords = optimum geometry disposition decision criteria

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17 pages, 1908 KiB  
Article
A Quadrilateral Geometry Classification Method and Device for Femtocell Positioning Networks
by Jeich Mar, Tsung Yu Chang and Yu Jie Wang
Sensors 2017, 17(4), 817; https://doi.org/10.3390/s17040817 - 10 Apr 2017
Cited by 2 | Viewed by 3909
Abstract
This article proposes a normalization multi-layer perception (NMLP) geometry classifier to autonomously determine the optimal four femtocell evolved Node Bs (FeNBs), which can use time difference of arrival (TDOA) to measure the location of the macrocell user equipment (MUE) with the lowest GDOP [...] Read more.
This article proposes a normalization multi-layer perception (NMLP) geometry classifier to autonomously determine the optimal four femtocell evolved Node Bs (FeNBs), which can use time difference of arrival (TDOA) to measure the location of the macrocell user equipment (MUE) with the lowest GDOP value. The iterative geometry training (IGT) algorithm is designed to obtain the training data for the NMLP geometry classifier. The architecture of the proposed NMLP geometry classifier is realized in the server of the cloud computing platform, to identify the optimal geometry disposition of four FeNBs for positioning the MUE located between two buildings. Six by six neurons are chosen for two hidden layers, in order to shorten the convergent time. The feasibility of the proposed method is demonstrated by means of numerical simulations. In addition, the simulation results also show that the proposed method is particularly suitable for the application of the MUE positioning with a huge number of FeNBs. Finally, three quadrilateral optimum geometry disposition decision criteria are analyzed for the validation of the simulation results. Full article
(This article belongs to the Special Issue Innovative Sensing Control Scheme for Advanced Materials)
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