Higher Order Modulation Recognition Using Approximate Entropy

Open Access
Pawar, Saurabh
Graduate Program:
Electrical Engineering
Master of Science
Document Type:
Master Thesis
Date of Defense:
November 17, 2010
Committee Members:
  • John F Doherty, Thesis Advisor
  • cognitive radio
  • modulation classification
  • approximate entropy
Modulation recognition finds its application in today’s cognitive systems ranging from civilian to military installations. Existing modulation classification algorithms include classic likelihood approaches and feature based approaches. In this study, Approximate Entropy (ApEn), which is a non-linear method to analyze a time series, is proposed as a robust feature of a modulation scheme. ApEn is used as a feature to identify parameters such as number of symbol levels, pulse lengths and modulation indices of a continuous phase modulation (CPM) signal. The method is then extended to classify CPM signals with differing pulse shapes which include raised cosine and Gaussian pulses with varying roll-off factors and bandwidth-time products respectively. The extracted features result in high classification accuracies for a variety of signals and performs robustly even in the presence of synchronization errors and carrier phase offsets. The ApEn scheme is further applied to other modulation scheme as well such as frequency shift keying (FSK), phase shift keying (PSK) and quadrature amplitude modulations (QAM) and performance results for intra-class and inter-class classification are presented.