Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
On the combinatorics of cumulants
Journal of Combinatorial Theory Series A
Automatic Modulation Recognition of Communication Signals
Automatic Modulation Recognition of Communication Signals
Combined likelihood power estimation and multiple hypothesis modulation classification
ASILOMAR '95 Proceedings of the 29th Asilomar Conference on Signals, Systems and Computers (2-Volume Set)
Blind identification of FIR channels carrying multiple finite alphabet signals
ICASSP '95 Proceedings of the Acoustics, Speech, and Signal Processing, 1995. on International Conference - Volume 02
Higher-order cyclic cumulants for high order modulation classification
MILCOM'03 Proceedings of the 2003 IEEE conference on Military communications - Volume I
Transmitter induced cyclostationarity for blind channelequalization
IEEE Transactions on Signal Processing
Training feedforward networks with the Marquardt algorithm
IEEE Transactions on Neural Networks
Underdetermined BSS of MISO OSTBC Signals
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Automatic modulation recognition using wavelet transform and neural networks in wireless systems
EURASIP Journal on Advances in Signal Processing
Hi-index | 0.00 |
We derive and analyze a new pattern recognition approach for automatic modulation recognition of MPSK (2, 4, and 8) signals in broad-band Gaussian noise. Presented method is based on constellation rotation of the received symbols, and a 4th order cumulant of a 1D distribution of the signal's in-phase component. Using Fourier series expansion of this cumulant as a function of the rotation angle, we extract invariant features which are then used in a neural classifier. Discrimination power of the proposed set of features is verified through extensive simulations, and the performance of the suggested algorithm is compared to the maximum-likelihood (ML) classifiers. Corresponding results show that our technique is comparable to the coherent ML classifier and outperforms the non-coherent pseudo-ML method for all considered signal-to-noise ratio (SNR) without the computational overhead of the latter.