Grey systems theory applications to wireless communications

This paper discusses grey systems theory (GST) applications in wireless communications and highlights its potential to cognitive radio. GST consists of information theory concepts and practical algorithms developed to address situations where information is incomplete and affected by random uncertainties. Two GST concepts, grey relational analysis (GRA) and grey model (GM) prediction theory are discussed. GRA provides a method to quantify the similarity between a reference data series and set of data while GM is used for modeling time series data and enables prediction of future values with limited data points and unknown probability distributions. These two techniques are surveyed with respect to their applications to wireless communications. Their application to predictive Cognitive Radio and as a similarity measure for case based reasoning cognitive engines is highlighted. A GRA based Automatic Modulation Classification (AMC) algorithm is applied to digital communications signals with preliminary results shown in simulation.

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