|Automatic Modulation Classifier - A Blind Feature-Based Tool
|Master of Science, Miami University, 2016, Computational Science and Engineering|
Automatic modulation classifiers (AMC) are one of the basic building blocks of electronic warfare receivers and cognitive radios. Although many research papers on AMC algorithms have been published, very few results on their implementation are available. This thesis presents a feature-based AMC built upon a software-defined radio platform. The developed AMC can detect signals over a broad spectrum and classify the modulation used. The modulation schemes considered in this thesis are amplitude modulation (AM), frequency modulation (FM), phase-shift keying (PSK), and quadrature amplitude modulation (QAM). Experimental results demonstrate the validity of the developed AMC algorithm and its implementation.
Committee: Chi-Hao Cheng, Ph.D (Advisor); Dmitriy Garmatyuk, Ph.D (Committee Member); Jason Pennington, Ph.D (Committee Member)
Communication; Computer Engineering; Computer Science; Electrical Engineering; Engineering; Experiments; Technology
Keywords: Software Defined Radio; NI USRP-2920; USRP; Modulation; Automatic Modulation Detection; Automatic Modulation Classification; AMC; High Order Statistics; LabVIEW; Implementation; Electronic Warfare; Cognitive Radio; AM; FM; PSK; QAM; Energy Analyzer; SNR