As many coded systems operate at very low signal-to-noise ratios, synchronization becomes a very difficult task. In many cases, conventional algorithms will either require long training sequences or result in large BER degradations. By exploiting code properties, these problems can be avoided. In this contribution, we present several iterative maximum-likelihood (ML) algorithms for joint carrier phase estimation and ambiguity resolution. These algorithms operate on coded signals by accepting soft information from the MAP decoder. Issues of convergence and initialization are addressed in detail. Simulation results are presented for turbo codes, and are compared to performance results of conventional algorithms. Performance comparisons are carried out in terms of BER performance and mean square estimation error (MSEE). We show that the proposed algorithm reduces the MSEE and, more importantly, the BER degradation. Additionally, phase ambiguity resolution can be performed without resorting to a pilot sequence, thus improving the spectral efficiency.
This article is part of the series Turbo Processing.
Iterative Code-Aided ML Phase Estimation and Phase Ambiguity Resolution
Digital Communications Research Group, Department of Telecommunications and Information Processing, Ghent University, Sint-Pietersnieuwstraat 41, Ghent 9000, Belgium
EURASIP Journal on Advances in Signal Processing 2005, 2005:895349 doi:10.1155/ASP.2005.981
The electronic version of this article is the complete one and can be found online at: http://asp.eurasipjournals.com/content/2005/6/895349
Received: | 29 September 2003 |
Revisions received: | 25 May 2004 |
Published: | 15 May 2005 |
© 2005 Wymeersch and Moeneclaey
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