Open AccessDEEP LEARNING FOR AUTOMATIC RF-MODULATION CLASSIFICATION
Author(s) -
Mihai-Octavian Dima,
T. Dima
Publication year - 2021
Publication title -
9th international conference "distributed computing and grid technologies in science and education"
Language(s) - English
Resource type - Conference proceedings
DOI - 10.54546/mlit.2021.28.31.001
Subject(s) - perceptron , least significant bit , modulation (music) , computer science , usb , artificial intelligence , amplitude modulation , quadrature amplitude modulation , frequency modulation , multilayer perceptron , artificial neural network , pattern recognition (psychology) , speech recognition , software , radio frequency , algorithm , acoustics , physics , telecommunications , bit error rate , decoding methods , programming language , operating system
Classical methods use statistical-moments to determine the type of modulation in question. Thisessentially correct approach for discerning amplitude modulation (AM) from frequency modulation(FM) fails for more demanding cases such as AM vs. AM-LSB (lower side-band rejection) - radiosignals being richer in information than statistical moments. Parameters with good discriminatingpower were selected in a data conditioning phase and binary deep-learning classifiers were trained forAM-LSB vs. AM-USB, FM vs. AM, AM vs. AM-LSB, etc. The parameters were formed asfeatures, from wave reconstruction primary parameters: rolling pedestal, amplitude, frequency andphase. Very encouraging results were obtained for AM-LSB vs. AM-USB with stochastic training,showing that this particularly difficult case (inaccessible with stochastic moments) is well solvablewith multi-layer perceptron (MLP) neuromorphic software.