Automatic Modulation Classification Using Grey Relational Analysis

One component of wireless communications of increasing necessity in both civilian and military applications is the process of automatic modulation classification. Modulation of a detected signal of unknown origin requiring interpretation must first be determined before the signal can be demodulated. This thesis presents a novel architecture for a modulation classifier that determines the most likely modulation using Grey Relational Analysis with the extraction and combination of multiple signal features. An evaluation of data preprocessing methods is conducted and performance of the classifier is investigated with the addition of each new signal feature used for classification.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Electrical and Computer Engineering

Department dc:contributor.department

Electrical and Computer Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2011

Author dc:creator

• Price, Matthew

Chair dc:contributor.committeechair

• Reed, Jeffrey H.

Committee members dc:contributor.committeemember

• Bose, Tamal
• Amanna, Ashwin E.

• Grey Relational Analysis
• Automatic Modulation Classification
• Haar Wavelet Transform
• Cumulants
• Cyclostationary Analysis

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Dc Identifier Other

etd-05032011-103859

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/42441