Modulation recognition in continuous phase modulation using approximate entropy

Saurabh U. Pawar, John F. Doherty

Research output: Contribution to journalArticle

28 Citations (Scopus)

Abstract

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, a nonlinear method to analyze a time series, is proposed as a unique characteristic of a modulation scheme. It is projected as a robust feature to identify signal parameters such as number of symbol levels, pulse lengths, and modulation indices of a continuous phase modulated (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. This approximate entropy feature-based approach results in high classification accuracies for a variety of signals and performs robustly even in the presence of synchronization errors and carrier phase offsets. Results are presented in the form of extensive simulations.

Original languageEnglish (US)
Article number5871718
Pages (from-to)843-852
Number of pages10
JournalIEEE Transactions on Information Forensics and Security
Volume6
Issue number3 PART 1
DOIs
StatePublished - Sep 1 2011

Fingerprint

Phase modulation
Entropy
Modulation
Cognitive systems
Time series
Synchronization
Bandwidth

All Science Journal Classification (ASJC) codes

  • Safety, Risk, Reliability and Quality
  • Computer Networks and Communications

Cite this

@article{88a2289e08434e22895255e468acdc46,
title = "Modulation recognition in continuous phase modulation using approximate entropy",
abstract = "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, a nonlinear method to analyze a time series, is proposed as a unique characteristic of a modulation scheme. It is projected as a robust feature to identify signal parameters such as number of symbol levels, pulse lengths, and modulation indices of a continuous phase modulated (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. This approximate entropy feature-based approach results in high classification accuracies for a variety of signals and performs robustly even in the presence of synchronization errors and carrier phase offsets. Results are presented in the form of extensive simulations.",
author = "Pawar, {Saurabh U.} and Doherty, {John F.}",
year = "2011",
month = "9",
day = "1",
doi = "10.1109/TIFS.2011.2159000",
language = "English (US)",
volume = "6",
pages = "843--852",
journal = "IEEE Transactions on Information Forensics and Security",
issn = "1556-6013",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "3 PART 1",

}

Modulation recognition in continuous phase modulation using approximate entropy. / Pawar, Saurabh U.; Doherty, John F.

In: IEEE Transactions on Information Forensics and Security, Vol. 6, No. 3 PART 1, 5871718, 01.09.2011, p. 843-852.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Modulation recognition in continuous phase modulation using approximate entropy

AU - Pawar, Saurabh U.

AU - Doherty, John F.

PY - 2011/9/1

Y1 - 2011/9/1

N2 - 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, a nonlinear method to analyze a time series, is proposed as a unique characteristic of a modulation scheme. It is projected as a robust feature to identify signal parameters such as number of symbol levels, pulse lengths, and modulation indices of a continuous phase modulated (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. This approximate entropy feature-based approach results in high classification accuracies for a variety of signals and performs robustly even in the presence of synchronization errors and carrier phase offsets. Results are presented in the form of extensive simulations.

AB - 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, a nonlinear method to analyze a time series, is proposed as a unique characteristic of a modulation scheme. It is projected as a robust feature to identify signal parameters such as number of symbol levels, pulse lengths, and modulation indices of a continuous phase modulated (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. This approximate entropy feature-based approach results in high classification accuracies for a variety of signals and performs robustly even in the presence of synchronization errors and carrier phase offsets. Results are presented in the form of extensive simulations.

UR - http://www.scopus.com/inward/record.url?scp=80051718199&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=80051718199&partnerID=8YFLogxK

U2 - 10.1109/TIFS.2011.2159000

DO - 10.1109/TIFS.2011.2159000

M3 - Article

AN - SCOPUS:80051718199

VL - 6

SP - 843

EP - 852

JO - IEEE Transactions on Information Forensics and Security

JF - IEEE Transactions on Information Forensics and Security

SN - 1556-6013

IS - 3 PART 1

M1 - 5871718

ER -