Automatic Modulation Recognition of PSK Signals Using Nonuniform Compressive Samples Based on High Order Statistics

TL;DR

This paper introduces a novel approach for automatic modulation recognition of PSK signals by leveraging phase sparsity and nonuniform compressive sampling based on high order statistics, enabling sub-Nyquist sampling.

Contribution

It establishes a new phase sparsity-based model for PSK signals and applies compressed sensing theory to the phase domain, reducing sampling rates significantly.

Findings

Model verified through simulations

Sampling rate scales with symbol rate, not bandwidth

Supports phase sparsity in compressed sensing applications

Abstract

Phase modulation is a commonly used modulation mode in digital communication, which usually brings phase sparsity to digital signals. It is naturally to connect the sparsity with the newly emerged theory of compressed sensing (CS), which enables sub-Nyquist sampling of high-bandwidth to sparse signals. For the present, applications of CS theory in communication field mainly focus on spectrum sensing, sparse channel estimation etc. Few of current researches take the phase sparse character into consideration. In this paper, we establish the novel model of phase modulation signals based on phase sparsity, and introduce CS theory to the phase domain. According to CS theory, rather than the bandwidth, the sampling rate required here is scaling with the symbol rate, which is usually much lower than the Nyquist rate. In this paper, we provide analytical support for the model, and simulations verify its validity.