- Research Article
- Open access
- Published:
Iterative Code-Aided ML Phase Estimation and Phase Ambiguity Resolution
EURASIP Journal on Advances in Signal Processing volume 2005, Article number: 895349 (2005)
Abstract
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.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
About this article
Cite this article
Wymeersch, H., Moeneclaey, M. Iterative Code-Aided ML Phase Estimation and Phase Ambiguity Resolution. EURASIP J. Adv. Signal Process. 2005, 895349 (2005). https://doi.org/10.1155/ASP.2005.981
Received:
Revised:
Published:
DOI: https://doi.org/10.1155/ASP.2005.981