Power prediction using an optimal neuro-fuzzy predictor

This paper presents a neuro-fuzzy predictor for received power level prediction in mobile communication systems. An important but difficult problem in designing such predictor is to determine the complexity of the predictor structure, i.e., the number of input nodes and the number of membership functions needed for each input node. We solve this problem by using the predictive Minimum Description Length (PMDL) principle. This results in a predictor with excellent generalization capability. The optimized neuro-fuzzy predictor is then used for power prediction of simulated Rayleigh lading signals with 1.8 GHz carrier frequency. The results show that our optimized predictor can provide very accurate predictions of received signal power. Our neuro-fuzzy predictor is well suitable for applications where efficient compensation of fast fading and accurate power control are required.