Deep radio signal clustering with interpretability analysis based on saliency map

Abstract

With the development of information technology, radio communication technology has made rapid progress. Many radio signals that have appeared in space are difficult to classify without manually labeling. Unsupervised radio signal clustering methods have recently become an urgent need for this situation. Meanwhile, the high complexity of deep learning makes it difficult to understand the decision results of the clustering models, making it essential to conduct interpretable analysis. This paper proposed a combined loss function for unsupervised clustering based on autoencoder. The combined loss function includes reconstruction loss and deep clustering loss. Deep clustering loss is added based on reconstruction loss, which makes similar deep features converge more in feature space. In addition, a features visualization method for signal clustering was proposed to analyze the interpretability of autoencoder utilizing Saliency Map. Extensive experiments have been conducted on a modulated signal dataset, and the results indicate the superior performance of our proposed method over other clustering algorithms. In particular, for the simulated dataset containing six modulation modes, when the SNR is 20 ​dB, the clustering accuracy of the proposed method is greater than 78%. The interpretability analysis of the clustering model was performed to visualize the significant features of different modulated signals and verified the high separability of the features extracted by clustering model.