Verification and Recognition of Fractal Characteristics of Communication Modulation Signals

With the rapid development of software radio and communication technologies, wireless communication environment is becoming more complicated. How to accurately identify communication modulation signals under low SNR environment has become a hot topic in current research. Fractal is an effective tool to describe the geometric irregularity and geometric scale characteristics, and feature extraction of signals has become possible by fractal theory. However, whether the communication signals have fractal characteristics, and whether the fractal feature can be used to achieve accurate feature extraction of signals is still a problem worth exploring. This paper first took QPSK signal as an example, and used mathematical methods to prove that the communication modulation signals have fractal characteristics. Then, an improved fractal box dimension algorithm was used to extract and recognize five signals to verify the effectiveness of fractal theory based feature extraction. Simulation results illustrate that the recognition result can achieve 97.8% even under the SNR of 10dB environment. This provides a theoretical basis for the wide application of fractal theory in the field of signal identification.