Fractal And Higher-order Statistics-based Digital Communications Signal Standard Identification

Modulation identification for communication signals is a technology related to many communication fields. The objection of modulation identification is, without any prior knowledge about signals'information contents, to determine modulation formats of communication signals exactly, including the extraction of signals'characteristics and the effective identification of selected signals based on characteristic parameters.Referring to a large number of literatures, this dissertation selected seven types of digital communication signals—BPSK, QPSK, OQPSK, MSK, GMSK, 2FSK, 4FSK, which are often used in communication and difficult to be distinguished. Then this dissertation focused on analyzing the difference among signals'modulation formats. Based on the characteristics of higher-order cumulant that can suppress white Gaussian noise, and of the fractal dimension that is not sensitive to noise, and through theoretical derivation and analysis, this dissertation selected six-order cumulant and the fractal box dimension as characteristic parameters for identification. According to the selected characteristic parameters, a valid identification algorithm was proposed, and neural network was used to identify signals'modulation formats. Simulation confirmed the feasibility of the algorithm.