Use APEX neural networks to extract the PN sequence in lower SNR DS-SS signals

  • Authors:

  • Tianqi Zhang;Zengshan Tian;Qianbin Chen;Xiaokang Lin;Zhengzhong Zhou

  • Affiliations:

  • School of Communication and Information Engineering, Research Centre for Optical Internet and Wireless Information Networks, Chongqing University of Posts and Telecommunications, Chongqing, China;School of Communication and Information Engineering, Research Centre for Optical Internet and Wireless Information Networks, Chongqing University of Posts and Telecommunications, Chongqing, China;School of Communication and Information Engineering, Research Centre for Optical Internet and Wireless Information Networks, Chongqing University of Posts and Telecommunications, Chongqing, China;Graduate School at Shenzhen, Tsinghua University, Shenzhen, China;School of Communication and Information Engineering, University of Electronic Science and Technology of China, Chengdu, China

  • Venue:
  • ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
  • Year:
  • 2006

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Abstract

This paper introduces an unsupervised adaptive principal components analysis (APEX) neural network (NN) for blind pseudo noise (PN) sequence extraction of lower signal to noise ratios (SNR) direct sequence spread spectrum (DS-SS) signals. The proposed method is based on eigen-analysis of DS-SS signals. As the eigen-analysis method is based on the decomposition of autocorrelation matrix of signals, it has computational defects when the signal vectors became longer, etc. So, we introduce the APEX NN to extract the PN sequence blindly. We also make complexity analysis of the proposed method and comparison with the other methods. Theoretical analysis and computer simulations verify the effectiveness of the method.