A Review on the Division of Autonomic Modulation in the Cognitive Radio: Classification of Modulation Schemes using Cyclostationary Features and Neural Networks

Abstract

Cognitive Radios have become a key research area in communications over the past few years. They play an important role in dynamic spectrum management and interference identification. Automatic Modulation Classification is the automatic recognition of the modulation format of a sensed signal. Most modulated signals exhibit the property of Cyclostationary that can be exploited for the purpose of classification. A feature-based method called Cyclostationary Feature Detection is able to classify different modulation schemes. The Spectral Correlation Function obtained from the sensed signal is used as a cyclic feature. The Cycle frequency Domain Profile derived from Spectral Correlation Function is used as a discriminator in the classification process since several modulation schemes have unique cycle frequency domain profiles. The neural network approach based on the learning mechanism is employed for pattern matching. It is used for classification of data patterns and distinguishing them into predefined set of lasses. The two layered neural network is trained using the Back Propagation Algorithm.