Automatic Modulation Recognition of PSK signals using nonuniform compressive samples based on high order statistics

@article{Xing2014AutomaticMR,
  title={Automatic Modulation Recognition of PSK signals using nonuniform compressive samples based on high order statistics},
  author={Zhengli Xing and Jie Zhou and Jiangfeng Ye and Jun Yan and Lin Zou and Qun Wan},
  journal={2014 IEEE International Conference on Communiction Problem-solving},
  year={2014},
  pages={611-614}
}
  • Zhengli Xing, Jie Zhou, +3 authors Q. Wan
  • Published 1 December 2014
  • Mathematics, Physics, Computer Science
  • 2014 IEEE International Conference on Communiction Problem-solving
The Nth Power Nonlinear Transformation (NPT) is a common method for automatic modulation classification, especially for PSK signals. However, greater than Nyquist rate sampling is essential for features extraction in NPT. In this paper, introducing the compressive sensing (CS) theory, we propose a novel Automatic Modulation Recognition (AMR) method based on the frequency spectrum of the Nth power nonlinear transformation of PSK type signals, from nonuniform compressive samples. Here, analysis… 
Features extraction for PSK signals recognition using nonuniform compressive samples based on high order transformation
TLDR
This paper shows that the Nth power nonlinear features used for PSK signals are compressible in the Amplitude domain and proposes a novel Automatic Modulation Recognition (AMR) method which based on the N fourth power non linear features of PSK type signals by introducing the compressive sensing (CS) theory.
Automatic Modulation Recognition for Phase Shift Keying Signals With Compressive Measurements
  • Lijin Xie, Q. Wan
  • Physics, Computer Science
    IEEE Wireless Communications Letters
  • 2018
TLDR
The existence, position and amplitude of fourth-order spectrum lines are utilized as the robust features for AMR work and the proposed AMR algorithm with compressive measurements in the framework of compressive sensing is performed.
Adaptivity at the Physical Layer

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