Evaluation of features for the automatic recognition of OFDM signals in monitoring or cognitive receivers

• Autorzy: Liedtke F. , Albers U.
• Języki publikacji: EN

Abstrakty

EN

The automatic recognition of signal types is an important task of monitoring receivers and also cognitive receivers. Several modulation recognition or classification procedures exist for single channel signal types while a simple robust procedure for automatic recognition of OFDM signals is lacking because of its numerous frequency channelslying close together. The task considered in this paper is the discrimination between OFDM (or multi-channel) signals and other signal types. The number of frequency channels of the OFDM signals is assumed to be unknown a priori. So, together with the automatic OFDM detection the estimation of the number of frequency channels is treated. Several discrimination features have been examined and the most promising ones are described: measures of the variation, of the skewness, of the kurtosis, and of the specific picket-fence shape of the spectrum which is typical for many OFDM signals. For a number of real-world OFDM samples, recorded from the high frequency range, results are presented. An automatic discrimination from single channel or noise like signals is achieved and the number of system channels can be estimated.

Słowa kluczowe

EN

OFDM signal recognition; discrimination features; cepstrum evaluation; estimation of frequency channel number

• Wydawca: Instytut Łączności - Państwowy Instytut Badawczy
• Czasopismo: Journal of Telecommunications and Information Technology
• Rocznik: 2008
• Tom: nr 2
• Strony: 30--36
• Opis fizyczny: Bibliogr. 3 poz., rys., tab.

Twórcy

autor

Liedtke F.

autor

Albers U.

• Research Institute for Communications, Information Processing, and Ergonomics (FKIE), Research Establishment for Applied Science (FGAN), Neuenahrer st 20, D-53343 Wachtberg-Werthhoven, Germany,

Bibliografia

• [1] F. Liedtke, “Adaptive procedure for automatic modulation recognition”, J. Telecommun. Inform. Technol., no. 4, pp. 91–97, 2004.
• [2] M. L. D. Wong and A. K. Nandi, “Automatic digital modulation recognition using artificial neural network and genetic algorithm”, Sig. Proces., vol. 84, pp. 351–365, 2004.
• [3] A. M. Mood, F. A. Graybill, and D. C. Boes, Introduction to the Theory of Statistics, 3rd ed. New York: McGraw-Hill, 1974.