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Enhanced modulation classification algorithm based on Kolmogorov-Smirnov test
- Ahn, Seongjin;
- Lee, Jaeyoon;
- Yoon, Dongweon;
- Choi, Jun Won
Citations
SCOPUS
6초록
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)
키워드
Automatic modulation classification; Kolmogorov-Smirnov test; Mean square error; Gaussian noise (electronic); Modulation; White noise; Additive white Gaussian noise channel; Automatic modulation classification; Classification performance; Cumulative distribution; Decision statistics; Kolmogorov-Smirnov test; Mean Square Error (MSE); Modulation classification; Mean square error
- 제목
- Enhanced modulation classification algorithm based on Kolmogorov-Smirnov test
- 저자
- Ahn, Seongjin; Lee, Jaeyoon; Yoon, Dongweon; Choi, Jun Won
- 발행일
- 2017-12
- 유형
- Conference Paper
- 저널명
- International Conference on Information and Communication Technology Convergence: ICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017
- 권
- 2017-December
- 페이지
- 232 ~ 234