A Novel Compressed Sensing Based Model for Reconstructing Sparse Signals Using Phase Sparse Character

TL;DR

This paper introduces a new compressed sensing model that leverages phase sparsity in phase modulation signals, enabling sub-Nyquist sampling based on phase domain characteristics, with analytical and simulation validation.

Contribution

It proposes a novel phase sparsity-based model for phase modulation signals and applies compressed sensing theory to reduce sampling rates in digital communication.

Findings

Sampling rate scales with symbol rate, not bandwidth.

Model validity confirmed through analytical analysis.

Simulations demonstrate effective signal reconstruction.

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.