论文信息 - A comparison of NN-based and SVR-based power prediction for mobile DS/CDMA systems

A comparison of NN-based and SVR-based power prediction for mobile DS/CDMA systems

We further investigate the performances of our previously proposed technique for received signal power prediction in the direct sequence code division multiple access (DS/CDMA) systems based on support vector regression (SVR.) The scheme is based on one-step ahead prediction using the past values of signal series as the inputs. The predictor parameters are chosen by considering the minimum mean square error (MMSE.) We compare the performances of the proposed predictor to that of the linear and nonlinear neural network-based predictors, i.e., the adaptive linear (Adaline) predictor, multilayer perceptrons (MLP) predictor and the hybrid predictor (Adaline cascade with MLP.) The carrier frequency of 1.8 GHz and a noisy Rayleigh fading channel are considered. The vehicle speeds are set to 5 km/h and 50 km/h. Cross validation is also applied to improve the prediction performance of our technique. The results on the blind test data show that the SVR-based predictor using the five-fold cross validation yields the best prediction performance among the aforementioned predictors.

[1] Ahmadreza Khoogar,et al. A Comparison of Adaline and MLP Neural Network based Predictors in SIR Estimation in Mobile DS/CDMA Systems , 2007 .

[2] R. Brereton,et al. Support vector machines for classification and regression. , 2010, The Analyst.

[3] Maggi Kelly,et al. Support vector machines for predicting distribution of Sudden Oak Death in California , 2005 .

[4] N. Theera-Umpon,et al. Power prediction in reverse link for mobile DS/CDMA systems using support vector regression , 2008, 2008 5th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology.

[5] Timothy W. Simpson,et al. Analysis of support vector regression for approximation of complex engineering analyses , 2003, DAC 2003.

[6] Branka Vucetic,et al. Spread spectrum CDMA systems for wireless communications , 1997 .

[7] N. Theera-Umpon,et al. Short-term load forecasting using wavelet transform and support vector machines , 2007, 2007 International Power Engineering Conference (IPEC 2007).

[8] Nipon Theera-Umpon,et al. Leeway Prediction of Oceanic Disastrous Target via Support Vector Regression , 2004, Journal of Advanced Computational Intelligence and Intelligent Informatics.

[9] Xiao Zhi Gao,et al. Power prediction in mobile communication systems using an optimal neural-network structure , 1997, IEEE Trans. Neural Networks.