Modulation recognition with alpha-stable noise over fading channels

In this paper, we propose a novel method to recognize the digital modulation communication signals with alpha-stable noise over fading channels. Specifically, the probability density function of alpha-stable noise is determined by kernel density estimation, and the fading channel parameters are measured by utilizing the improved expectation conditional maximization algorithm. Then we leverage the compound hypothesis test model, which serves as a classifier, to implement modulation recognition. Simulation results show that the proposed method is robust to alpha-stable noise over fading channels. Moreover, the correct average recognition rates of the digital modulation communication signals under alpha-stable noise is more than 90%, when the generalized signal-to-noise ratio is 10dB.

[1]  Yaohua Hu,et al.  Modulation classification in Alpha stable noise , 2016, 2016 IEEE 13th International Conference on Signal Processing (ICSP).

[2]  Xiao-Li Meng,et al.  Maximum likelihood estimation via the ECM algorithm: A general framework , 1993 .

[3]  Victor C. M. Leung,et al.  Spectrum Sensing Based on Maximum Generalized Correntropy Under Symmetric Alpha Stable Noise , 2019, IEEE Transactions on Vehicular Technology.

[4]  W. Linde.  STABLE NON‐GAUSSIAN RANDOM PROCESSES: STOCHASTIC MODELS WITH INFINITE VARIANCE , 1996 .

[5]  Titir Dutta,et al.  A novel method for automatic modulation classification under non-Gaussian noise based on variational mode decomposition , 2016, 2016 Twenty Second National Conference on Communication (NCC).

[6]  Anoop Kumar Krishna,et al.  Automatic modulation classification for cognitive radios using cumulants based on fractional lower order statistics , 2011, 2011 XXXth URSI General Assembly and Scientific Symposium.

[7]  Hao Song,et al.  Data-Driven Deep Learning for Signal Classification in Industrial Cognitive Radio Networks , 2021, IEEE Transactions on Industrial Informatics.

[8]  Lingfei Zhang,et al.  Modulation Recognition with Alpha-Stable Noise Over Fading Channels , 2021, 6GN.

[9]  F. Richard Yu,et al.  Modified Cramér-Rao Bound for $M$-FSK Signal Parameter Estimation in Cauchy and Gaussian Noise , 2019, IEEE Transactions on Vehicular Technology.

[10]  Hao Song,et al.  Intelligent Signal Classification in Industrial Distributed Wireless Sensor Networks-Based IIoT , 2020 .

[11]  J. L. Nolan,et al.  Numerical calculation of stable densities and distribution functions: Heavy tails and highly volatil , 1997 .

[12]  Juan C. Agüero,et al.  On maximum likelihood estimation of channel impulse response and carrier frequency offset in OFDM systems , 2017, 2017 IEEE 9th Latin-American Conference on Communications (LATINCOM).

[13]  Yunfei Chen,et al.  Modulation Parameter Estimation of LFM Interference for Direct Sequence Spread Spectrum Communication System in Alpha-Stable Noise , 2020, IEEE Systems Journal.

[14]  Su Hu,et al.  Resource and Trajectory Optimization for Secure Communications in Dual Unmanned Aerial Vehicle Mobile Edge Computing Systems , 2022, IEEE Transactions on Industrial Informatics.

[15]  Luiz F. Q. Silveira,et al.  Automatic Modulation Classification Architectures Based on Cyclostationary Features in Impulsive Environments , 2019, IEEE Access.

[16]  Benoît Champagne,et al.  UFMC-based wideband spectrum sensing for cognitive radio systems in non-Gaussian noise , 2017, 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC).

[17]  Shih-Chun Lin,et al.  Automatic Modulation Classification Under Non-Gaussian Noise: A Deep Residual Learning Approach , 2019, ICC 2019 - 2019 IEEE International Conference on Communications (ICC).

[18]  F. Richard Yu,et al.  Blind Parameter Estimation of M-FSK Signals in the Presence of Alpha-Stable Noise , 2020, IEEE Transactions on Communications.

[19]  Yiyang Pei,et al.  Robust Modulation Classification under Uncertain Noise Condition Using Recurrent Neural Network , 2018, 2018 IEEE Global Communications Conference (GLOBECOM).

[20]  Mohsen Guizani,et al.  Automatic Modulation Classification for Low SNR Digital Signal in Frequency-Selective Fading Environments , 2015, Wirel. Pers. Commun..

[21]  SaiDhiraj Amuru,et al.  A Blind Preprocessor for Modulation Classification Applications in Frequency-Selective Non-Gaussian Channels , 2015, IEEE Transactions on Communications.

[22]  Jie Liu,et al.  A Faster Maximum-Likelihood Modulation Classification in Flat Fading Non-Gaussian Channels , 2019, IEEE Communications Letters.

[23]  Dingcheng Yang,et al.  Wireless Information and Power Transfer: Optimal Power Control in One-Way and Two-Way Relay System , 2015, Wireless Personal Communications.

[24]  Qian Chen,et al.  Automatic digital modulation recognition in the presence of alpha-stable noise , 2020, Phys. Commun..

[25]  Weidang Lu,et al.  SWIPT Cooperative Spectrum Sharing for 6G-Enabled Cognitive IoT Network , 2021, IEEE Internet of Things Journal.

[26]  Brian W. Pogue,et al.  Directional Kernel Density Estimation for Classification of Breast Tissue Spectra , 2017, IEEE Transactions on Medical Imaging.

[27]  I. Podkurkov,et al.  Adaptive classification of digital amplitude-phase modulated signals with additive non-Gaussian noise , 2017, 2017 Systems of Signal Synchronization, Generating and Processing in Telecommunications (SINKHROINFO).