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Improved modulation classification using a factor-graph-based iterative receiver

We bring together two research topics which have been the focus of significant research individually: modulation classification and iterative receiver design. In this work, these topics are joined within the framework of factor graphs which provide a unified approach to representing a variety of algorithms, especially iterative algorithms. Specifically, in this paper we present a factor graph which incorporates modulation classification into the iterative receiver structure. The proposed iterative receiver applies message passing on the factor graph to approximate the optimal solution to joint modulation classification, demodulation, and decoding. This results in a classifier which treats feedback from the decoder as a priori probabilities for the coded bits. We show that the proposed receiver is able to achieve significant performance gains over a receiver which performs maximum likelihood classification separately from demodulation and decoding.