Automatic Modulation Recognition of Digital Communication Signals Based on the First Statistical Moments

Automatic modulation recognition of digital communication signals is extremely important for both military and civilian purposes. In this paper, seven key features based on the first statistical moments of the intercepted signals are proposed to automatically recognize MASK (M-ary Amplitude Shift Keying), MFSK (M-ary Frequency Shift Keying), MPSK (M-ary Phase Shift Keying) and MQAM (M-ary Quadrature Amplitude Modulation) signals. All these key features are calculated using the conventional signal processing methods. Compared to those based on the second or even higher order moments, the calculation of the proposed seven key features used in the modulation recognition algorithm has been less complicated and more efficient. A modulation identification algorithm based on the decision-theoretic approach is also developed which removes the need for symbol synchronization. Computer simulations have been carried out.  It has shown that all the aforementioned four types of digital communication signals have been recognized with average success rate ＞97% at SNR≥7dB. Thus it is superior to some other algorithms and is suitable for the practical application of signal detection and fast recognition in non-cooperation communication systems.