IncepSeqNet: Advancing Signal Classification with Multi-Shape Augmentation (Student Abstract)
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.
Cite
Text
Kim and Jo. "IncepSeqNet: Advancing Signal Classification with Multi-Shape Augmentation (Student Abstract)." AAAI Conference on Artificial Intelligence, 2024. doi:10.1609/AAAI.V38I21.30464Markdown
[Kim and Jo. "IncepSeqNet: Advancing Signal Classification with Multi-Shape Augmentation (Student Abstract)." AAAI Conference on Artificial Intelligence, 2024.](https://mlanthology.org/aaai/2024/kim2024aaai-incepseqnet/) doi:10.1609/AAAI.V38I21.30464BibTeX
@inproceedings{kim2024aaai-incepseqnet,
title = {{IncepSeqNet: Advancing Signal Classification with Multi-Shape Augmentation (Student Abstract)}},
author = {Kim, Jongseok and Jo, Ohyun},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2024},
pages = {23542-23543},
doi = {10.1609/AAAI.V38I21.30464},
url = {https://mlanthology.org/aaai/2024/kim2024aaai-incepseqnet/}
}