Multi Channel Electronic Communication Signal Parameters based on Nonlinear Phase Principle Modulation and Deep Learning

Abstract

In order to solve the problem of high sampling rate and large number of sampling points required by current phase modulation signal parameter estimation methods, a parameter modulation method for multi-channel electronic communication signals based on nonlinear phase principle and deep learning is proposed. Firstly, classify and introduce the modulation methods, and propose a new algorithm for identifying instantaneous feature parameters. The author conducted nonlinear phase principle modulation recognition on seven typical digital signals: 2ASK, 4ASK, 2FSK, 4FSK, 2PSK, 4PSK, and 16QAM. Using the author’s algorithm, experiments were conducted on the recognition of seven digital nonlinear phase modulation signals under different signal-to-noise ratios. As can be seen from the results, when the signal-to-noise ratio is greater than or equal to 10dB, the recognition accuracy of the seven digital nonlinear phase modulation signals can reach 100%, verifying that the new algorithm proposed by the author improves the recognition accuracy.