Automatic Modulation Recognition of PSK Signals with Sub-Nyquist Sampling Based on High Order Statistics

2014/12/31 by Zhengli Xing, Xing, Zhengli, Jie Zhou +11
Computer Science · Engineering · #Blind Source Separation Techniques #FOS: Computer and information sciences #Information Theory (cs.IT) #Sparse and Compressive Sensing Techniques #Wireless Signal Modulation Classification

paper · pdf · doi:10.48550/arxiv.1501.00158

openalex publication_date 2014/12/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Sampling rate required in the Nth Power Nonlinear Transformation (NPT) method is typically much greater than Nyquist rate, which causes heavy burden for the Analog to Digital Converter (ADC). Taking advantage of the sparse property of PSK signals' spectrum under NPT, we develop the NPT method for PSK signals with Sub-Nyquist rate samples. In this paper, combined the NPT method with Compressive Sensing (CS) theory, frequency spectrum reconstruction of the Nth power nonlinear transformation of PSK signals is presented, which can be further used for AMR and rough estimations of unknown carrier frequency and symbol rate.

Citations

• Stable signal recovery from incomplete and inaccurate measurements

Related

• Automatic Modulation Recognition of PSK Signals Using Nonuniform Compressive Samples Based on High Order Statistics
• A Novel Compressed Sensing Based Model for Reconstructing Sparse Signals Using Phase Sparse Character
• RIP Analysis of Modulated Sampling Schemes for Recovering Spectrally Sparse Signals
• Compressive phase retrieval of sparse bandlimited signals
• Random Triggering Based Sub-Nyquist Sampling System for Sparse Multiband Signal