A spectral opportunities forecasting method in a mobile network based on the integration of COST 231 Walfisch-Ikegami and wavelet neural models

The forecast of radioelectric spectrum occupancy is useful in the design of wireless systems that profit the opportunities in the spectrum as in the cognitive radio. In the current paper, the development of a method is proposed, that through the forecast of the reception power, identifies the spectral opportunities in a channel of a mobile cellular network for an urban environment. The proposed method integrates the COST 231 Walfisch-Ikegami (C231-W-I) large-scale propagation model with a wavelet neural model. The method results, obtained through simulations, are consistent with the observed behavior in experiments of this kind of wireless systems.

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