Soft Information Improvement for PN Sequence Iterative Acquisition

Iterative message passing algorithms (iMPAs) which are generalized from the well-known turbo principle can reach a rapid pseudo-noise (PN) sequence acquisition at low computational complexity. However, its performance will degrade at low signal-to-noise ratio (SNR). In this paper, a soft information...

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Bibliographic Details
Published in2010 International Conference on Computational Intelligence and Security pp. 533 - 536
Main Authors Wei Wang, Nianke Zong, Jie Tang, Lambotharan, S
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2010
Subjects
Complexity theory
Delay
iterative message passing
Maximum likelihood estimation
PN sequence acquisition
Signal to noise ratio
Simulation
soft information
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Summary:Iterative message passing algorithms (iMPAs) which are generalized from the well-known turbo principle can reach a rapid pseudo-noise (PN) sequence acquisition at low computational complexity. However, its performance will degrade at low signal-to-noise ratio (SNR). In this paper, a soft information improvement using multiple samples in one chip is proposed. Meanwhile, to mitigate the timing error which will affect the information improvement, a Maximum-Likelihood (ML) estimation without significant increase on the complexity is introduced. Simulation results show that proposed method can realize rapid PN code acquisition at lower SNR than existing method.
ISBN:9781424491148
1424491142
DOI:10.1109/CIS.2010.122