Enhanced modulation classification algorithm based on Kolmogorov-Smirnov test
- Authors
- Ahn, Seongjin; Lee, Jaeyoon; Yoon, Dongweon; Choi, Jun Won
- Issue Date
- Dec-2017
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- Automatic modulation classification; Kolmogorov-Smirnov test; Mean square error
- Citation
- International Conference on Information and Communication Technology Convergence: ICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017, v.2017-December, pp.232 - 234
- Indexed
- SCOPUS
- Journal Title
- International Conference on Information and Communication Technology Convergence: ICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017
- Volume
- 2017-December
- Start Page
- 232
- End Page
- 234
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/18565
- DOI
- 10.1109/ICTC.2017.8190976
- ISSN
- 0000-0000
- Abstract
- We propose an enhanced automatic modulation classification algorithm based on Kolmogorov-Smirnov test. The proposed classifier employs the real and imaginary components extracted from the received signal as separate decision statistics. Also, unlike the conventional K-S test based algorithm, mean square error (MSE) between the empirical cumulative distribution and the hypothesized distribution for each modulation candidate is evaluated in the proposed algorithm. Simulation results show that the proposed algorithm provides better classification performance than the conventional K-S test based algorithm in an additive white Gaussian noise (AWGN)
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