IncepSeqNet: Advancing Signal Classification with Multi-Shape Augmentation (Student Abstract)

Authors

  • Jongseok Kim Chungbuk National University, Cheongju, Republic of Korea
  • Ohyun Jo Chungbuk National University, Cheongju, Republic of Korea

DOI:

https://doi.org/10.1609/aaai.v38i21.30464

Keywords:

Deep Learning, 5G, Imagification, Augmentation, Inception

Abstract

This work proposes and analyzes IncepSeqNet which is a new model combining the Inception Module with the innovative Multi-Shape Augmentation technique. IncepSeqNet excels in feature extraction from sequence signal data consisting of a number of complex numbers to achieve superior classification accuracy across various SNR(Signal-to-Noise Ratio) environments. Experimental results demonstrate IncepSeqNet’s outperformance of existing models, particularly at low SNR levels. Furthermore, we have confirmed its applicability in practical 5G systems by using real-world signal data.

Published

2024-03-24

How to Cite

Kim, J., & Jo, O. (2024). IncepSeqNet: Advancing Signal Classification with Multi-Shape Augmentation (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 38(21), 23542-23543. https://doi.org/10.1609/aaai.v38i21.30464