Automatic modulation classification of higher order QAM signals

• Main Author: Gan, Eng Siew
• Other Authors: Gong Yi
• Format: Final Year Project
• Language: English
• Published: 2011
• Subjects: DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
• Online Access: http://hdl.handle.net/10356/45999
• Institution: Nanyang Technological University

Modulation classification (MC) is the recognition of the modulation type of an input signal. Modulation classification has been used for more than 2 decades in military applications such as electronic warfare, surveillance, threat analysis, spectrum monitoring and management. As an example, it is known that the jamming and anti-jamming procedures can be done more efficiently by knowing the modulation type of an intercepted signal. Modulation classification has also many commercial applications. It plays an important role in programmable and reconfigurable systems, particularly in the form of software defined radios, which need to cope with the variety of communication systems with different modulations schemes. Modulation classification is also used in cognitive radios as a part of the spectrum sensing unit. In cognitive radios, in order to efficiently use of spectrum resources, secondary users sense and share the spectrum with the legacy licensed primary users. Without reliable spectrum sensing, secondary users can cause unintentional interference to the primary user or other secondary users. Recently, with software defined radios and cognitive radios being increasingly used in number of applications, modulation classification has received renewed interest. Among the different digital modulations, the family of QAM modulations are of great attention as they have been used widely in new standards. In this correspondence, we review the different modulation classification methods, with the focus on the methods used to distinguish between QAM signals for the case of additive white Gaussian noise channel. Several strategies dealing with channel phase offset, channel frequency offset and time delay are presented. The rest of this report is structured as follows. The next sections in this chapter present the signal model and QAM Modulation used in the report. Chapter II contains the different methods of modulation classification as well as the techniques for dealing with the unknown channel parameters. Simulation results are presented in chapter III. Chapter IV conclude the report.