Abstract:
Modulation types clustering (MC) is crucial for adaptive high-frequency communication between devices in the Industrial Internet of Things. The strength of MC resides in ...Show MoreMetadata
Abstract:
Modulation types clustering (MC) is crucial for adaptive high-frequency communication between devices in the Industrial Internet of Things. The strength of MC resides in its self-supervised framework, enabling it to extract modulation features efficiently without any manual labeling. However, the misalignment of proxy tasks and erroneous pseudolabeling constrain the performance of prevalent MC feature extraction techniques that utilize time series signals. In this article, we compare the saliency maps on time–frequency image (TFI) with that on time series signal, highlighting the consistency of TFI reconstruction with modulation feature extraction. Subsequently, in order to address the sensitivity of K-means to outliers, Sinkhorn–Knopp labeling (SKLb) is proposed to balance the scale of clusters and neighboring distances. Moreover, in consideration of the potential instability of the SKLb iteration result in backpropagation, the Sinkhorn–Knopp loss is proposed to ensure stable training of the model. Finally, two models, SK-IDC and SK-STDC, were tested on four datasets. Experimental results on these datasets present that our approach outperforms original signal representation and prevalent deep clustering methods, achieving State-of-the-Art performance.
Published in: IEEE Transactions on Industrial Informatics ( Volume: 20, Issue: 11, November 2024)