Classification of digital modulated signals based on time frequency representation

This paper proposes a new method for classifying Digital Modulations, including the typical PSK (Phase Shift Keying), FSK (Frequency Shift Keying), ASK (Amplitude Shift Keying) as well as the present OFDM (Orthogonal Frequency Division Multiplex) modulation. The method is based on the analysis of th...

Full description

Bibliographic Details
Main Authors: Haq, K., Mansour, A., Nordholm, Sven
Format: Conference Paper
Published: 2010
Online Access:http://hdl.handle.net/20.500.11937/47974
_version_ 1848757982223925248
author Haq, K.
Mansour, A.
Nordholm, Sven
author_facet Haq, K.
Mansour, A.
Nordholm, Sven
author_sort Haq, K.
building Curtin Institutional Repository
collection Online Access
description This paper proposes a new method for classifying Digital Modulations, including the typical PSK (Phase Shift Keying), FSK (Frequency Shift Keying), ASK (Amplitude Shift Keying) as well as the present OFDM (Orthogonal Frequency Division Multiplex) modulation. The method is based on the analysis of the time frequency representation of the digitally modulated signals. At first, some experiments have been done to monitor the time frequency representation for different types of modulations. Then a statistical method has been applied and finally a peak detection technique has been employed to classify the modulation types. The method is capable to classify PSK, ASK, FSK 2, FSK 4, FSK 8, FSK16 and OFDM signals. Finally many simulations have been conducted and it is shown that, our method is capable to classify the right modulation against an SNR (Signal to Noise Ratio) of less than 5 dB. The classification rate is 100% for PSK and ASK signals, and 96.5% for OFDM signals. No explicit prior information is required for this method. ©2010 IEEE.
first_indexed 2025-11-14T09:36:44Z
format Conference Paper
id curtin-20.500.11937-47974
institution Curtin University Malaysia
institution_category Local University
last_indexed 2025-11-14T09:36:44Z
publishDate 2010
recordtype eprints
repository_type Digital Repository
spelling curtin-20.500.11937-479742017-09-13T14:15:45Z Classification of digital modulated signals based on time frequency representation Haq, K. Mansour, A. Nordholm, Sven This paper proposes a new method for classifying Digital Modulations, including the typical PSK (Phase Shift Keying), FSK (Frequency Shift Keying), ASK (Amplitude Shift Keying) as well as the present OFDM (Orthogonal Frequency Division Multiplex) modulation. The method is based on the analysis of the time frequency representation of the digitally modulated signals. At first, some experiments have been done to monitor the time frequency representation for different types of modulations. Then a statistical method has been applied and finally a peak detection technique has been employed to classify the modulation types. The method is capable to classify PSK, ASK, FSK 2, FSK 4, FSK 8, FSK16 and OFDM signals. Finally many simulations have been conducted and it is shown that, our method is capable to classify the right modulation against an SNR (Signal to Noise Ratio) of less than 5 dB. The classification rate is 100% for PSK and ASK signals, and 96.5% for OFDM signals. No explicit prior information is required for this method. ©2010 IEEE. 2010 Conference Paper http://hdl.handle.net/20.500.11937/47974 10.1109/ICSPCS.2010.5709731 restricted
spellingShingle Haq, K.
Mansour, A.
Nordholm, Sven
Classification of digital modulated signals based on time frequency representation
title Classification of digital modulated signals based on time frequency representation
title_full Classification of digital modulated signals based on time frequency representation
title_fullStr Classification of digital modulated signals based on time frequency representation
title_full_unstemmed Classification of digital modulated signals based on time frequency representation
title_short Classification of digital modulated signals based on time frequency representation
title_sort classification of digital modulated signals based on time frequency representation
url http://hdl.handle.net/20.500.11937/47974