MuShAug: Boosting Sequence Signal Classification via Multishape Augmentation

The utilization of sequence signals in real-world mobile communications plays a crucial role in the design and optimization of communication methods. Through our own performance evaluation, we have confirmed that conventional augmentation techniques, mainly designed for image or photograph data, are unsuitable for sequence signal applications due to inherent differences in data characteristics. To address this practical limitation, multishape augmentation (MuShAug) employs sequence signal-to-image (SSI) to represent sequence signals in image format, enabling the extraction of diverse signal features. To evaluate the practical applicability of our proposed method, we conduct experiments using real-world sequence signals collected from operational Fifth Generation (5G) mobile communication systems. In experimental trials, MuShAug consistently demonstrates robust generalization performance, achieving high levels of classification accuracy. Furthermore, through the incorporation of random phase transformation (RPT), our method achieves further enhanced performance within advanced data augmentation techniques.