摘要
The rapid growth of unlabeled time-series data in domains such as wireless communications,radar,biomedical engineering,and the Internet of Things(IoT)has driven advancements in unsupervised learning.This review synthe-sizes recent progress in applying autoencoders and vision transformers for un-supervised signal analysis,focusing on their architectures,applications,and emerging trends.We explore how these models enable feature extraction,anomaly detection,and classification across diverse signal types,including electrocardiograms,radar waveforms,and IoT sensor data.The review high-lights the strengths of hybrid architectures and self-supervised learning,while identifying challenges in interpretability,scalability,and domain generaliza-tion.By bridging methodological innovations and practical applications,this work offers a roadmap for developing robust,adaptive models for signal in-telligence.
