On time-series analysis and signal classification - part I: fractal dimensions

Time series analysis is becoming an increasingly reliable tool for the study of complicated dynamics in measurements across many fields of science and engineering. This paper explores the applications of nonlinear time series analysis for digital communication signal classification. In particular, the fractal dimension was investigated as a tool for signal classification. Primarily, the fractal dimension was calculated for a set of white Gaussian noise as well as for a pure sinusoid. The effect of added DC component as well as the noise variance on the fractal dimension was also investigated. Moreover, the fractal dimension of a set of simulated signals is calculated and investigated for possible use as tool for modulation classification. Furthermore, a time-domain feed-forward neural network was trained and tested for digital signal classification. The success rate of the neural network was used as benchmark for assessment. The method is applied to several examples of synthetic signals, of digital modulations such as ASK, FSK, BPSK, QPSK, and 16PSK.