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Related Experiment Videos

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

X M Gao1, X Z Gao, J A Tanskanen

  • 1Lab. of Telecommun. Technol., Helsinki Univ. of Technol., Espoo.

IEEE Transactions on Neural Networks
|January 1, 1997
PubMed
Summary

This study introduces a novel neural network predictor for direct sequence code division multiple access (DS/CDMA) systems. The optimized predictor effectively reduces noise and improves signal quality for power control applications.

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Area of Science:

  • Electrical Engineering
  • Signal Processing
  • Artificial Intelligence

Background:

  • Received power level prediction is crucial for direct sequence code division multiple access (DS/CDMA) systems.
  • Designing cascade neural network predictors requires determining optimal network complexity.
  • Noise attenuation and generalization capability are key challenges in signal prediction.

Purpose of the Study:

  • To develop a novel neural-network-based predictor for received power level prediction in DS/CDMA systems.
  • To optimize neural network complexity using the predictive minimum description length (PMDL) principle.
  • To evaluate the predictor's performance in mitigating noise and improving signal quality.

Main Methods:

  • A cascade predictor architecture combining an adaptive linear element (Adaline) and a multilayer perceptron (MLP).

Related Experiment Videos

  • Application of the predictive minimum description length (PMDL) principle for optimal node selection.
  • Predictive filtering of noisy Rayleigh fading signals at a 1.8 GHz carrier frequency.

    • Main Results:

    • The optimized neural predictor achieved significant signal-to-noise ratio (SNR) gains for in-phase and quadrature signals.
    • SNR gains of approximately 12 dB and 7 dB were observed at 5 km/h and 50 km/h, respectively.
    • Power signal SNR gains of about 11 dB and 5 dB were recorded at the same speeds.

    Conclusions:

    • The proposed neural predictor offers effective noise attenuation and excellent generalization capabilities.
    • The predictor is well-suited for power control applications requiring delay-less noise reduction and fast fading mitigation.
    • The PMDL principle provides an effective method for optimizing neural network complexity in signal processing applications.