A neural network receiver for EM-MWD baseband communication systems

Authors:
Timothy Whitacre;Xiao-Hua Yu
Affiliations:
Department of Electrical Engineering, California Polytechnic State University, San Luis Obispo, CA;Department of Electrical Engineering, California Polytechnic State University, San Luis Obispo, CA
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

Citing 4
Cited 0

Digital communications: fundamentals and applications

Digital communications: fundamentals and applications
Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
Adaptive digital communication receivers

IEEE Communications Magazine
Backpropagation Algorithms for a Broad Class of Dynamic Networks

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Baseband digital communication in "electro-magnetic measurement while drilling" 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.