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

TL;DR

This paper introduces a novel approach combining Nth Power Nonlinear Transformation and Compressive Sensing to perform automatic modulation recognition of PSK signals using sub-Nyquist sampling, reducing ADC burden.

Contribution

It develops a sub-Nyquist sampling method for PSK signals by integrating NPT with CS, enabling spectrum reconstruction and modulation recognition at lower sampling rates.

Findings

Effective spectrum reconstruction of PSK signals at sub-Nyquist rates.

Accurate modulation recognition and rough parameter estimation.

Reduced ADC sampling requirements for PSK signal analysis.

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.