Automatic Modulation Classification: Principles, Algorithms and Applications

8Comparison of Modulation Classifiers

8.1 Introduction

In Chapters 3–7 we listed an array of modulation classifiers. While their mechanisms are distinctly different and intriguing, we are more interested in their actual modulation-classification performance. Modulation classification may be applied in many different scenarios; the traits of a good modulation classifier are shared in most cases.

First, a modulation classifier should be able to classify as many modulation types as possible. Such a trait makes a modulation classifier easily applicable in different applications without needing any modification to accommodate extra modulations. Second, a modulation classifier should provide high classification accuracy. The high classification accuracy is relative to the different noise levels. Third, the modulation classifier should be robust in many different channel conditions. The robustness can be provided by either the built-in channel estimation and correction mechanism or the natural resilience of the modulation classifier against channel conditions. Fourth, the modulation classifier should be computationally efficient. In many applications, there is a strict limitation of computation power which may be unsuitable for over-complicated modulation classifiers. Meanwhile, some applications may require fast decision making, which requires the classification to be evaluated swiftly. Only a modulation classifier with high computational efficiency could meet this requirement. After ...