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Communications computing

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  • Electrical and electronic engineering [4]
  • Electromagnetic fields [4]
  • Electromagnetic waves, antennas and propagation [4]
  • Electromagnetic wave propagation [4]
  • Radiowave propagation [4]
  • Communications [4]
  • Telecommunication [4]
  • Radio links and equipment [4]
  • Mobile radio systems [4]
  • Computer and control engineering [4]
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Neural network approach to prediction of terrestrial wave propagation for mobile radio
Author(s): K.E. Stocker, B.E. Gschwendtner, F.M. Landstorfer
Source: IEE Proceedings H (Microwaves, Antennas and Propagation), Volume 140, Issue 4, 1993 , p. 315 - 320
Type: Article
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Theoretical investigations into the suitability of neural network simulators for the prediction of field strength based on topographical and morphographical data are presented. Effective input and output data processing is developed using deterministic and heuristic formulae for the training of a neural network simulator. First successful steps towards a planning tool for mobile radio communication based on adaptive learning from measurement data are made. Field strength prediction with the new system shows acceptable conformity with measurements.

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Erratum: Empirical prediction of radiowave propagation by neural network simulator
Author(s): K.E. Stocker, F.M. Landstorfer
Source: Electronics Letters, Volume 28, Issue 12, June 1992, p. 1176 - 1176
Type: Article
Empirical prediction of radiowave propagation by neural network simulator
Author(s): K.E. Stocker, F.M. Landstorfer
Source: Electronics Letters, Volume 28, Issue 12, June 1992, p. 1177 - 1178
Type: Article
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A first step towards a planning tool for mobile radio communication based on adaptive learning from measurement data is presented.

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Empirical prediction of radiowave propagation by neural network simulator
Author(s): K.E. Stocker, F.M. Landstorfer
Source: Electronics Letters, Volume 28, Issue 8, April 1992, p. 724 - 726
Type: Article
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A first step towards a planning tool for mobile radio communication based on adaptive learning from measurement data is presented.

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