This paper introduces two algorithms for analog and digital modulations recognition. The first algorithm utilizes the decision-theoretic approach in which a set of decision criteria for identifying different types of modulations is developed. In the second algorithm the artificial neural network (ANN) is used as a new approach for the modulation recognition process. Computer simulations of different types of band-limited analog and digitally modulated signals corrupted by band-limited Gaussian noise sequences have been carried out to measure the performance of the developed algorithms. In the decision-theoretic algorithm it is found that the overall success rate is over 94% at the signal-to-noise ratio (SNR) of 15 dB, while in the ANN algorithm the overall success rate is over 96% at the SNR of 15 dB.

Algorithms for automatic modulation recognition of communication signals

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. It is shown that the nth moment (n even) of the phase of the signal is a monotonic increasing function of M. On the basis of this property, the authors formulate a general hypothesis test, develop a decision rule, and derive an analytic expression for the probability of misclassification. Two examples are given to demonstrate the performance of the algorithm. The algorithm is compared with the quasi-log-likelihood radio (qLLRC), square-law (SLC), and phase-based (PBC) classifiers. The algorithm is outperformed by qLLRC at low CNR but is comparable to SLC and is better than PBC. The qLLRC algorithm is only valid at CNR >

Signal classification using statistical moments

This paper addresses automatic modulation classification for PSK and QAM signals under coherent and noncoherent conditions. In particular, the paper extends previous results by treating the classification of higher-state QAM signals. A maximum-likelihood algorithm is presented for coherent classification of PSK and QAM signals. We evaluate the algorithms performance for various PSK and QAM modulation types including 64-state QAM and then compare it with a psuedo maximum-likelihood noncoherent classification technique in terms of the error rate, false alarm rate, and computational complexity. The application of these results to the design and performance of an automatic signal recognizer is discussed throughout the paper.

Maximum-likelihood modulation classification for PSK/QAM

Modulation recognition using artificial neural networks

Automatic analogue modulation recognition

Identification of the modulation type of a signal

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.

L.G Gadbois

Papers

Identification of the modulation type of a signal

Identification of the modulation type of a signal