Research Article
Modulation Recognition with Alpha-Stable Noise Over Fading Channels
@INPROCEEDINGS{10.1007/978-3-030-63941-9_24, author={Lingfei Zhang and Mingqian Liu and Jun Ma and Chengqiao Liu}, title={Modulation Recognition with Alpha-Stable Noise Over Fading Channels}, proceedings={6GN for Future Wireless Networks. Third EAI International Conference, 6GN 2020, Tianjin, China, August 15-16, 2020, Proceedings}, proceedings_a={6GN}, year={2021}, month={1}, keywords={Cognitive radio Modulation recognition Fading channel Non-Gaussian noise Alpha stable distribution}, doi={10.1007/978-3-030-63941-9_24} }- Lingfei Zhang
Mingqian Liu
Jun Ma
Chengqiao Liu
Year: 2021
Modulation Recognition with Alpha-Stable Noise Over Fading Channels
6GN
Springer
DOI: 10.1007/978-3-030-63941-9_24
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%.
