Automatic Modulation Recognition Based on Multi-Dimensional Feature Extraction

摘要

As an intermediate step between signal detection and demodulation, automatic modulation recognition (AMR) is commonly used in cognitive radio networks to identify different types of communication modulation. A new automatic modulation scheme is proposed, based on decision tree theory, which is a general method for different types of band-limited Gaussian noise modulation types. In particular, by combining the instantaneous statistic feature and high-order cumulants feature, the key features are extracted to realize the blind recognition of analog and digital signals. In addition, a new characteristic parameter AT is proposed to improve the performance of modulation recognition under low signal-to-noise ratio (SNR). The simulation results show that, for all the analyzed signals, when the SNR reaches 3dB, the recognition success rate can reach more than 95%, reflecting the superiority of the method proposed in this paper.