Power prediction in mobile communication systems using an optimal neural-network structure

Abstract

This paper presents a novel neural-network-based predictor for received power level prediction in direct sequence code division multiple access (DS/CDMA) systems. The predictor consists of an adaptive linear element (Adaline) followed by a multilayer perceptron (MLP). An important but difficult problem in designing such a cascade predictor is to determine the complexity of the networks. We solve this problem by using the predictive minimum description length (PMDL) principle to select the optimal numbers of input and hidden nodes. This approach results in a predictor with both good noise attenuation and excellent generalization capability. The optimized neural networks are used for predictive filtering of very noisy Rayleigh fading signals with 1.8-GHz carrier frequency. Our results show that the optimal neural predictor can provide smoothed in-phase and quadrature signals with signal-to-noise ratio (SNR) gains of about 12 and 7 dB at the urban mobile speeds of 5 and 50 km/h, respectively. The corresponding power signal SNR gains are about 11 and 5 dB. Therefore, the neural predictor is well suitable for power control applications where "delayless" noise attenuation and efficient reduction of fast fading are required.

On Soft Computing Techniques in Various Areas

“…It is hoped that this gentle review will benefit computer scientist who are keen to contribute their works to the field of soft computing. [5], [6], [7] 3…”

Comparison of conventional and soft computing-based power control methods in mobile communications systems

“…In recent years, soft computing methods including neural networks and fuzzy logic have played a slowly emerging role in the ®eld of power prediction [8] and control [5] in mobile communications systems. Numerous new schemes, e.g., neural networks-based inverse system control and adaptive fuzzy control [5,7], have been proposed.…”

Industrial applications of soft computing: a review

“…Their predictor consists of an adaptive linear element followed by a multilayer perceptron. Besides, the neural network topologies are optimized using the predictive minimum description length (PMDL) method, for reducing computational complexity and maximizing the generalization capability [27]. …”