Title page for etd-0825111-140228


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URN etd-0825111-140228
Author Yu-sing Lin
Author's Email Address No Public.
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Department Communications Engineering
Year 2010
Semester 2
Degree Master
Type of Document
Language English
Title A Precoding Scheme Based on Perfect Sequences without Data Identification Problem for Data-Dependent Superimposed Training
Date of Defense 2011-07-25
Page Count 55
Keyword
  • Zadoff–Chu sequences
  • data-dependent superimposed training
  • inverse discrete Fourier transform
  • singular value decomposition
  • Abstract In data-dependent superimposed training (DDST) system, the data sequence subtracts a data-dependent sequence before transmission. The receiver cannot correctly find the unknown term which causes an error floor at high SNR.
    In this thesis, we list some helpful conditions to enhance the performance for precoding design in DDST system, and analyze the major cause of data misidentification by singular value decomposition (SVD) method. Finally, we propose a precoding matrix based on [C.-P. Li and W.-C. Huang, “A constructive representation for the Fourier dual of the Zadoff–Chu sequences,” IEEE Trans. Inf. Theory, vol. 53, no. 11, pp. 4221-4224, Nov. 2007]. The precoding matrix is constructed by an inverse discrete Fourier transform (IDFT) matrix and a diagonal matrix with the elements consist of an arbitrary perfect sequence. The proposed method satisfies these conditions and simulation results show that the data identification problem is solved.
    Advisory Committee
  • Yu-Te Su - chair
  • Jyh-Horng Wen - co-chair
  • Chin-Liang Wang - co-chair
  • Char-Dir Chung - co-chair
  • Chih-Peng Li - advisor
  • Files
  • etd-0825111-140228.pdf
  • Indicate in-campus at 5 year and off-campus access at 5 year.
    Date of Submission 2011-08-25

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