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Blind Phase-Amplitude Modulation Classification with Unknown Phase Offset

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2 Author(s)
M. L. D. Wong ; Swinburne University of Technology, Sarawak, Malaysia ; A. K. Nandi

This paper first discusses the maximum likelihood (ML) classifier for automatic classification of digital modulations. The classifier is optimum for classification of phase-amplitude modulated signals under ideal environment. However, this is not the case in the presence of phase offset owing to inaccurate estimation. In this paper, we propose a novel non-coherent ML classifier to mitigate the effect phase offset. The non-coherent ML classifier adopts a pre-classification phase correction stage through a closed form estimator based on higher order statistics. Experimental results show improvement of classification accuracy at reasonable signal to noise ratio

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18th International Conference on Pattern Recognition (ICPR'06)  (Volume:4 )

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