Journal of VLSI Signal Processing Systems
Channel prediction for OFDMA using mixtures of experts
International Journal of Knowledge-based and Intelligent Engineering Systems
Kalman-based power control for DS-CDMA cellular mobile systems
Mobility '07 Proceedings of the 4th international conference on mobile technology, applications, and systems and the 1st international symposium on Computer human interaction in mobile technology
Recurrent neural network based BER prediction for NLOS channels
Mobility '07 Proceedings of the 4th international conference on mobile technology, applications, and systems and the 1st international symposium on Computer human interaction in mobile technology
RNN based MIMO channel prediction
Signal Processing
Recurrent neural network based bit error rate prediction for narrowband fading channel
CSN '07 Proceedings of the Sixth IASTED International Conference on Communication Systems and Networks
Local linear regression for estimating time series data
Computational Statistics & Data Analysis
Hi-index | 0.00 |
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 ldquodelaylessrdquo noise attenuation and efficient reduction of fast fading are required