Robust automatic modulation classification and blind equalization: novel cognitive receivers

Abstract

The automatic modulation classifier (AMC) is an important signal processing component that helps the cognitive radio (CR) better utilize the spectrum. In a typical CR scenario, training sequence or channel knowledge is not available, and hence blind equalizers are widely used. The multipath fading channel not only affects symbol detection performance but also affects the performance of the AMC. In a conventional single input single output blind equalizer, the weights of the equalizer are adapted by minimizing cost functions that are non quadratic (multimodal). Convergence of the blind equalizer to a local minimum affects both symbol detection performance and AMC performance. In a CR scenario, it is preferable to consider the performance of the AMC also while adapting the blind equalizer weights. In this article, we propose novel CR receivers where the performance of the AMC is also considered while adapting the parameters of the blind equalizer. Computer simulations are given to illustrate the concept and yield promising results.