Hierarchical Automatic Modulation Classification under Hardware and Channel Impairments
- Authors
- Cho, Yunseol; Kim, Hanvit; Kim, 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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