Open AccessOptimized Polynomial Classifier for Classification of M-PSK Signals
Author(s) -
Nooh Bany Muhammad,
Mubashar Sarfraz,
Sajjad A. Ghauri,
Saqib Masood
Publication year - 2021
Publication title -
mathematical modelling and engineering problems/mathematical modelling of engineering problems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.26
H-Index - 11
eISSN - 2369-0747
pISSN - 2369-0739
DOI - 10.18280/mmep.080410
Subject(s) - rician fading , additive white gaussian noise , genetic algorithm , algorithm , polynomial , dimension (graph theory) , mathematics , pattern recognition (psychology) , rayleigh fading , computer science , white noise , fading , artificial intelligence , mathematical optimization , statistics , combinatorics , decoding methods , mathematical analysis
Automatic modulation classication (AMC) is the emerging research area for military and civil applications. In this paper, M-PSK signals are classied using the optimized polynomial classier. The distinct features i.e., higher order cumulants (HOC’s) are extracted from the noisy received signal and the dataset is generated with different number of samples, various SNR’s and on several fading channels. The proposed classier structure classies the overall modulation classication problem into binary sub-classications. In each sub-classication, the extracted features are expanded using polynomial expansion into higher dimension space. In higher dimension space numerous non-linearly separable classes becomes linearly separable. The performance of the proposed classier is evaluated on Rayleigh and Rician fading channels in the presence of additive white gaussian noise (AWGN). The polynomial classier performance is optimized using one of the famous heuristic computational techniques i.e., Genetic Algorithm (GA). The extensive simulations have been carried with and without optimization, which shows relatively better percentage classication accuracy (PCA) as compared with the state of art existing techniques.