Combined Blind Equalization and Automatic Modulation Classification for Cognitive Radios

Blind equalization and automatic modulation classification (AMC) have been of significant importance for cognitive radios when the receiver has no information about the channel or modulation type. Choosing an appropriate equalizer is difficult in the absence of channel information. In this paper, an AMC based on cyclostationary feature detection and a predictor-based recursive blind equalizer is used in conjunction. The probability of classification of the AMC is used as a metric and fed back to update the blind equalizer order. The equalizer and the AMC enhance the performance of each other. Computer simulations are given to illustrate the concept and yield promising results.