Application and effectiveness of weighted KNN in pattern recognition of communication modulated signals

Modulation pattern recognition of communication signals is a key technology in wireless communication, and the continuous complexity of the channel environment has put forward higher requirements on the communication signal modulation capability. This paper proposes a pattern recognition method of communication modulated signal based on weighted KNN. Firstly, signals of different modulation modes were generated in MATLAB. Then, the modulated signal features in the time and frequency domain were extracted, and the information entropy was included. Secondly, the extracted features were used to train and test the weighted KNN algorithm. By comparing the recognition effect of the modulated signals before and after signal feature extraction, it is concluded that feature extraction has a substantial improvement on the recognition accuracy and computing efficiency of weighted KNN, and different features have a significant effect on the recognition.