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Hierarchical Automatic Modulation Classification under Hardware and Channel Impairments

Authors
Cho, YunseolKim, HanvitKim, Sunwoo
Issue Date
Feb-2026
Publisher
IEEE Computer Society
Keywords
Automatic Modulation Classification; Deep Learning; Hardware Impairment
Citation
International Conference on ICT Convergence, pp 1470 - 1472
Pages
3
Indexed
SCOPUS
Journal Title
International Conference on ICT Convergence
Start Page
1470
End Page
1472
URI
https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/212330
DOI
10.1109/ICTC66702.2025.11387874
ISSN
2162-1233
2162-1241
Abstract
In this paper, we propose a hierarchical automatic modulation classification algorithm with robustness to hardware and channel impairments. The proposed algorithm improves classification performance through signal preprocessing that compensates for distortions caused by hardware and channel impairments. The algorithm yields additional performance gains with a hierarchical classification framework. The simulation results show that the proposed algorithm achieves enhanced classification performance compared to conventional non-hierarchical classifiers and demonstrates robustness under hardware and channel impairments.
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