• DocumentCode
    2857473
  • Title

    A temporal difference method-based prediction scheme applied to fading power signals

  • Author

    Gao, X.Z.

  • Author_Institution
    Inst. of Intelligent Power Electron., Helsinki Univ. of Technol., Espoo, Finland
  • Volume
    3
  • fYear
    1998
  • fDate
    4-9 May 1998
  • Firstpage
    1954
  • Abstract
    We first briefly discuss the operating principle of the temporal difference (TD) method. A TD method-based multi-step ahead prediction scheme using the modified Elman neural network (MENN) is then set up. This prediction approach provides for online adaptation and fast convergence rate. Next, it is applied to the prediction of the occurrence of long term deep fading in mobile communication systems. Simulation experiments reveal that our prediction scheme is capable of predicting the degree of occurrence possibility of deep fading. Based on this prediction result, the power control of cellular phone systems employing the reinforcement learning method will be investigated in the future
  • Keywords
    Rayleigh channels; cellular radio; code division multiple access; convergence; fading; feedforward neural nets; learning (artificial intelligence); power control; prediction theory; recurrent neural nets; telecommunication computing; time series; fading power signals; fast convergence rate; long term deep fading; mobile communication systems; modified Elman neural network; online adaptation; temporal difference method-based prediction scheme; Convergence; Fading; Mobile communication; Neural networks; Power electronics; Predictive models; Rayleigh channels; Supervised learning; Uniform resource locators; World Wide Web;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on
  • Conference_Location
    Anchorage, AK
  • ISSN
    1098-7576
  • Print_ISBN
    0-7803-4859-1
  • Type

    conf

  • DOI
    10.1109/IJCNN.1998.687158
  • Filename
    687158