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Feasibility of neural networks in modelling radio propagation for field strength prediction

✍ Scribed by A. P. Leros; A. A. Alexandridis; K. Dangakis; P. Kostarakis

Publisher: John Wiley and Sons
Year: 1998
Tongue: English
Weight: 349 KB
Volume: 11
Category: Article
ISSN: 1074-5351
DOI: 10.1002/(sici)1099-1131(199811/12)11:6<359::aid-dac377>3.0.co;2-9

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✦ Synopsis

A typical back-propagation neural network (BPN) model is developed for modelling radio propagation for field strength prediction based on data measurements of propagation loss (in decibels) with terrain information taken in an urban area (Athens region) in the 900 MHz band. The feasibility of the BPN model is checked against the performance of a conventional semiempirical reference model. The performance of both models is quantified by statistical methods. The evaluation is done by comparing their prediction error statistics of average absolute, standard deviation and root mean square and by comparing their percentage accuracy and correlation of predicted values relative to true data measurements.

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