ETD

Archivio digitale delle tesi discusse presso l'Università di Pisa

Tesi etd-07202007-181348


Tipo di tesi
Tesi di dottorato di ricerca
Autore
Principe, Fabio
Indirizzo email
fabio.principe@gmail.com, fabio.principe@iet.unipi.it
URN
etd-07202007-181348
Titolo
Iterative message-passing-based algorithms to detect spreading codes
Settore scientifico disciplinare
ING-INF/03
Corso di studi
INGEGNERIA DELL'INFORMAZIONE
Relatori
Relatore Prof. Luise, Marco
Parole chiave
  • SBAS
  • message-passing
  • m-sequences
  • GPS
  • gold codes
  • GNSS
  • CDMA
  • acquisition
  • spread-spectrum
Data inizio appello
25/05/2007
Consultabilità
Completa
Riassunto
This thesis tackles the issue of the rapid acquisition of spreading codes in Direct-Sequence Spread-Spectrum (DS/SS) communication systems. In particular, a new algorithm is proposed that exploits the experience of the iterative
decoding of modern codes (LDPC and turbo codes) to detect these sequences. This new method is a Message-Passing-based algorithm.
Specifically, instead of correlating the received signal with local replicas of the transmitted linear feedback shift register (LFSR) sequence, an iterative Message-Passing algorithm is implemented to be run on a loopy graph. In particular, these graphical models are designed by manipulating the generating polynomial structure of the considered LFSR sequence.
Therefore, this contribution is a detailed analysis of the detection technique based on Message-Passing algorithms to acquire m-Sequences and Gold codes. More in detail, a unified treatment to design and implement a specific set of graphical models for these codes is reported. A theoretical study on the acquisition time performance and their comparison to the standard algorithms (full-parallel, simple-serial, and hybrid searches) is done. A preliminary architectural design is also provided. Finally, the analysis is also enriched by comparing this new technique to the standard algorithms in terms of computational complexity and (missed/wrong/correct) acquisition probabilities as derived by simulations.
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