Abstract
This paper proposes a method based on kernel density estimation (KDE) and expectation condition maximization (ECM) to realize digital modulation recognition over fading channels with non-Gaussian noise in the cognitive radio networks. A compound hypothesis test model is adopt here. The KDE method is used to estimate the probability density function of non-Gaussian noise, and the improved ECM algorithm is used to estimate the fading channel parameters. Numerical results show that the proposed method is robust to the noise type over fading channels. Moreover, when the GSNR is 10 dB, the correct recognition rate for the digital modulation recognition under non-Gaussian noise is more than 90%. Gaussian noise, and the improved ECM algorithm is used to estimate the fading channel parameters. Numerical results show that the proposed method is robust to the noise type over fading channels. Moreover, when the GSNR is 10 dB, the correct recognition rate for the digital modulation recognition under non-Gaussian noise is more than 90%.
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Acknowledgments
The authors acknowledge the financial support of the Key Projects of R&D and Achievement Transformation in Qinghai Province (Grant: 2018-NN-151), the National Natural Science Foundation of China (Grant: 62071364 and 61761040).
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Zhang, L., Liu, M., Ma, J., Liu, C. (2020). Modulation Recognition with Alpha-Stable Noise Over Fading Channels. In: Wang, X., Leung, V.C.M., Li, K., Zhang, H., Hu, X., Liu, Q. (eds) 6GN for Future Wireless Networks. 6GN 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 337. Springer, Cham. https://doi.org/10.1007/978-3-030-63941-9_24
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DOI: https://doi.org/10.1007/978-3-030-63941-9_24
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