Signal Models for Modulation Classification

Summary

Signal models are the starting point of every meaningful modulation classification strategy. This chapter establishes some unified signal models for the development of all modulation classifiers, and provides a level ground for the validation of each modulation classifier. It institutes mathematical models of communication signals for three popular communication channels, namely additive white Gaussian noise (AWGN) channel, fading channel, and non‐Gaussian noise channel. The chapter presents a unified model of a fading channel with flexible representation of different fading scenarios. It also presents three non‐Gaussian noise models for modelling the impulsive noise. The chapter outlines Middleton's class A non‐Gaussian noise model as a complex but accurate modelling of impulsive noises. The symmetric alpha stable model is suggested as a simplified alternative to Middleton's class A model. In addition, the Gaussian mixture model is also established for analytical convenience in some of the complex modulation‐classification algorithms.