• DocumentCode
    2710530
  • Title

    A neural network receiver for EM-MWD baseband communication systems

  • Author

    Whitacre, Timothy ; Yu, Xiao-Hua

  • Author_Institution
    Dept. of Electr. Eng., California Polytech. State Univ., San Luis Obispo, CA, USA
  • fYear
    2009
  • fDate
    14-19 June 2009
  • Firstpage
    3360
  • Lastpage
    3364
  • Abstract
    Baseband digital communication in ldquoelectromagnetic measurement while drillingrdquo systems (EM-MWD) is often corrupted by surface noise. The conventional correlation receiver works well under the assumption of additive white Gaussian noise (AWGN); however in practice, the noise is actually non-stationary and usually contains spectral peaks in lower frequency range. In this research, a new approach based on artificial neural network is investigated. The neural network receiver has adaptive learning ability and outperforms the correlation receiver under various noise conditions, especially in the situation of non-white noise as well as the real world noise taken from actual drilling sites.
  • Keywords
    AWGN; digital communication; drilling; learning (artificial intelligence); measurement systems; neural nets; production engineering computing; receivers; EM-MWD baseband communication systems; adaptive learning; additive white Gaussian noise; artificial neural network; baseband digital communication; correlation receiver; drilling systems; electromagnetic measurement; neural network receiver; noise conditions; nonwhite noise; spectral peaks; surface noise; AWGN; Adaptive systems; Additive white noise; Artificial neural networks; Baseband; Digital communication; Frequency; Gaussian noise; Neural networks; Noise measurement;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2009. IJCNN 2009. International Joint Conference on
  • Conference_Location
    Atlanta, GA
  • ISSN
    1098-7576
  • Print_ISBN
    978-1-4244-3548-7
  • Electronic_ISBN
    1098-7576
  • Type

    conf

  • DOI
    10.1109/IJCNN.2009.5178838
  • Filename
    5178838