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Neural Computing and Applications

, Volume 30, Issue 3, pp 977–985 | Cite as

Direct sequence estimation: a functional network approach

  • Xiukai Ruan
  • Yanhua Tan
  • Guihua Cui
  • Wenbin Liu
  • Xiaojing Shi
  • Qibo Cai
  • Haitao Zhao
Original Article
  • 111 Downloads

Abstract

Functional networks (FNs) have shown excellent performance in probability, statistics, engineering applications, etc., but so far no methods of direct sequence estimation (DSE) for communication systems using FN have been published. The paper presents a new DSE approach using FN, which can be applied to cases with plural source signal sequence, short sequence or even the absence of training sequence. The proposed method can estimate the source sequence directly from the observed output data without training sequence and pre-estimating the channel impulse response. Firstly, a multiple-input multiple-output FN (MIMOFN), in which the initial input vector is devised via QR decomposition of receiving signal matrix, is adopted to solve the special issue. Meantime, a design method of the neural function for this special MIMOFN is proposed. Then, the learning rule for the parameters of neural functions is trained and updated by back-propagation learning algorithm. Finally, a simulation experiment is performed, the feasibility and accuracy of the method are showed from the experimental results, and some special simulation phenomena of the algorithm are observed.

Keywords

Direct sequence estimation MIMO Functional networks Neural function QAM 

Notes

Acknowledgements

The authors would like to acknowledge the financial support of this work from the National Natural Science Foundation of China (NSFC) (Grant Nos. 61671329, 61201426, 61501331), the Zhejiang Provincial Natural Science Foundation of China (Grant Nos. LQ16F010010 and LY16F010016), the Scientific Research Project of Education Department of Zhejiang Province of China (Grant Nos. Y201327231 and Y201430529). The authors also appreciate anonymous reviewers for their valuable and insightful comments, which were helpful for improving the paper.

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Copyright information

© The Natural Computing Applications Forum 2016

Authors and Affiliations

  • Xiukai Ruan
    • 1
    • 2
  • Yanhua Tan
    • 1
  • Guihua Cui
    • 1
  • Wenbin Liu
    • 1
  • Xiaojing Shi
    • 1
  • Qibo Cai
    • 1
    • 2
  • Haitao Zhao
    • 3
  1. 1.College of Physics and Electronic Information EngineeringWenzhou UniversityWenzhouChina
  2. 2.National-Local Joint Engineering Laboratory for Digitalize Electrical Design TechnologyWenzhouChina
  3. 3.College of Telecommunications and Information EngineeringNanjing University of Posts and TelecommunicationsNanjingChina

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