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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- Korean Institute of Information Scientists and Engineers Journal of Computing Science and Engineering Journal of Computing Science and Engineering Vol.16 No.1
- 발행연도
- 2022.3
- 수록면
- 25 - 42 (18page)
- DOI
- 10.5626/JCSE.2022.16.1.25
이용수
초록· 키워드
오류제보하기
Identification of signal waveforms is highly critical in 5G communications and other state-of-the-art radio technologies such as cognitive radios. For instance, to achieve efficient demodulation and spectrum sensing, cognitive radios need to implement automatic modulation recognition (AMR) of detected signals. Although many works have been reported in the literature on the subject, most of them have mainly focused on the additive white Gaussian noise (AWGN) channel. However, addressing the AWGN channel, only, does not sufficiently emulate real-time wireless communications. In this paper, we created datasets of six modulation schemes in GNU Radio. Wireless signal impairment issues such as center frequency offset, sample rate offset, AWGN, and multipath fading effects were applied for the dataset creation. Afterward, we developed AMR models by training different artificial neural network (ANN) architectures using real cepstrum coefficients (RCC), and minimum-phase reconstruction coefficients (MPRC) extracted from the created signals. Between these two features, MPRC features have the best performance, and the ANN architecture with Levenberg-Marquardt learning algorithm, as well as logsig and purelin activation functions in the hidden and output layers, respectively, gave the best performance of 98.7% accuracy, 100% sensitivity, and 99.33% specificity when compared with other algorithms. This model can be leveraged in cognitive radio for spectrum sensing and automatic selection of signal demodulators.
목차
Abstract
I. INTRODUCTION
II. MATERIALS AND METHODS
III. RESULTS
IV. DISCUSSION
V. CONCLUSION
REFERENCES
참고문헌 (33)
참고문헌 신청
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B. Ramkumar, “Automatic modulation classification for cognitive radios using cyclic feature detection,” IEEE Circuits and Systems Magazine, vol. 9, no. 2, pp. 27-45, 2009.
Y. Linn, “New structures for modulation classification and SNR estimation with applications to cognitive radio and software defined radio,” in Proceedings of 2014 IEEE 27th Canadian Conference on Electrical and Computer Engineering (CCECE), Toronto, Canada, 2014, pp. 1-8.
A. H. Kelechi, M. H. Alsharif, A. M. Ramly, N. F. Abdullah, and R. Nordin, “The four-C framework for high capacity ultra-low latency in 5G networks: a review,” Energies, vol. 12, no. 18, article no. 3449, 2019. https://doi.org/10.3390/en12183449
A. Hazza, M. Shoaib, S. A. Alshebeili, and A. Fahad, “An overview of feature-based methods for digital modulation classification,” in Proceedings of 2013 1st International Conference on Communications, Signal Processing, and their Applications (ICCSPA), Sharjah, UAE, 2013, pp. 1-6.
B. Ramkumar, “Automatic modulation classification for cognitive radios using cyclic feature detection,” IEEE Circuits and Systems Magazine, vol. 9, no. 2, pp. 27-45, 2009.
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