Automatic modulation classification method using fixed K-means algorithm for feature clustering processing.

ABSTRACT: The role played by communication technology in daily life is gradually increasing. However, there are problems such as complex types of signals, huge amount of data and noise interference, and the recognition accuracy of existing modulation classification methods is low. Therefore, the study proposes a signal automatic modulation classification model based on fixed K-mean algorithm and denoising autoencoder. The model uses fixed K-mean algorithm for feature classification and optimizes median filtering algorithm using dynamic thresholding. The classifier is used to improve the recognition accuracy of specific signals, and long short-term memory and data random corruption denoising are used to optimize the autoencoder. The experiments indicated that the signal classification accuracy of the

SUBMITTER: Yuan L 

PROVIDER: S-EPMC12520362 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature