Proceedings ArticleDOI
Identification of the modulation type of a signal
Yiu-Tong Chan,L.G Gadbois,P. Yansouni +2 more
- Vol. 10, pp 838-841
TLDR
Equations of R for four modulation types, as a function of the carrier to noise ratio, are derived and used to set up a classification scheme and confirm the effectiveness of the identification scheme.Abstract:
It is possible to recognize the modulation type of an unknown signal in noise by its envelope characteristics. A quantity found to be distinctive to a given modulation type is the ratio (R) of the variance of the envelope to the square of the mean of the envelope. Equations of R for four modulation types, as a function of the carrier to noise ratio, are derived and used to set up a classification scheme. The R of a received signal is first computed. Then its modulation type is determined according to the range R falls in. Simulation experiments have confirmed some of the theoretical development as well as the effectiveness of the identification scheme.read more
Citations
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Journal ArticleDOI
Signal classification using statistical moments
Samir S. Soliman,S.-Z. Hsue +1 more
TL;DR: An automatic modulation classification algorithm utilizing the statistical moments of the signal phase is developed and used to classify the modulation type of general M-ary PSK signals and is compared with the quasi-log-likelihood radio, square-law, and phase-based classifiers.
Journal ArticleDOI
Modulation recognition using artificial neural networks
TL;DR: In this chapter the artificial neural networks (ANNs) approach as another solution for the modulation recognition process is studied in some detail and it is suggested that the use of the ANN approach for solving the modulation recognised process may have better performance than the decision-theoretic approach.
Journal ArticleDOI
Automatic analogue modulation recognition
TL;DR: A global procedure for recognition of analogue modulation types is developed and it is found that all types of analog modulation have been classified with success rate ⩾ 90% at SNR = 10 dB.
Dissertation
Application of artificial intelligence to wireless communications
TL;DR: This dissertation provides the theory, design, and implementation of a cognitive engine, the enabling technology of cognitive radio, which provides a general framework to build and test cognitive engine algorithms and components such as sensing technology, optimization routines, and learning algorithms.
Proceedings ArticleDOI
Classification of co-channel communication signals using cyclic cumulants
TL;DR: In this article, a set of cyclic-cumulant-based features for signal classification is proposed and analyzed, and results of classification experiments using simulated data are presented, showing that each of a number of spectrally overlapping signals can be successfully classified by measuring and processing the proposed features.
References
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Book
Theory and application of digital signal processing
TL;DR: Feyman and Wing as discussed by the authors introduced the simplicity of the invariant imbedding method to tackle various problems of interest to engineers, physicists, applied mathematicians, and numerical analysts.
The automatic classification of modulation types by pattern recognition.
TL;DR: The new 'nearest neighbor' type of pattern recognizer has been developed that significantly increases classification accuracy and the decision surfaces of this classifier asymptotically approach the Bayes decision surfaces with simple set size.