A practical classification algorithm for M-ARY frequency shift keying signals

Zaihe Yu, Yun Q. Shi, Wei Su

Research output: Contribution to conferencePaperpeer-review

10 Scopus citations


In this paper, the issue of automatically classifying M-ary frequency shift keying (MFSK) signals contaminated by additive white Gaussian noise (AWGN) is considered, which is blind in nature since the modulation parameters of a received MFSK signal such as frequency deviation that may vary over a large range are unknown in advance. Based on the properties of the spectra of MFSK signals, we present a new classification algorithm which only requires some knowledge that can be obtained through the front-end processing. In simulation, the proposed algorithm can classify 2-, 4-, 8-, 16-, and 32-FSK signals with an overall success rate higher than 99% when the signal-to-noise ratio (SNR) is greater than or equal to 5 dB. To examine its effectiveness with MFSK data received from real-world communications systems, it has been applied to 2-, 4-, and 8-FSK data available in a website, resulting in an overall success rate of 89.1%. It is clear that the proposed algorithm has made a step towards practical and blind MFSK signal classification.

Original languageEnglish (US)
Number of pages6
StatePublished - 2004
EventMILCOM 2004 - 2004 IEEE Military Communications Conference - Monterey, CA, United States
Duration: Oct 31 2004Nov 3 2004


OtherMILCOM 2004 - 2004 IEEE Military Communications Conference
Country/TerritoryUnited States
CityMonterey, CA

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering


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