Wideband Power Spectrum Sensing: a Fast Practical Solution for Nyquist Folding Receiver

TL;DR

This paper introduces a fast, practical power spectrum sensing method for Nyquist folding receivers that leverages compressive covariance sensing and efficient algorithms, enabling real-time wideband spectrum analysis with reduced complexity.

Contribution

It proposes a novel, computationally efficient power spectrum estimation method for NYFR using compressive covariance sensing and simple operations, improving real-time wideband sensing.

Findings

Method reduces computational complexity linearly with sample size and sparsity.

Simulation shows the approach achieves accurate spectrum sensing in real-time.

Compared to existing algorithms, it offers lower complexity and practical implementation advantages.

Abstract

The limited availability of spectrum resources has been growing into a critical problem in wireless communications, remote sensing, and electronic surveillance, etc. To address the high-speed sampling bottleneck of wideband spectrum sensing, a fast and practical solution of power spectrum estimation for Nyquist folding receiver (NYFR) is proposed in this paper. The NYFR architectures is can theoretically achieve the full-band signal sensing with a hundred percent of probability of intercept. But the existing algorithm is difficult to realize in real-time due to its high complexity and complicated calculations. By exploring the sub-sampling principle inherent in NYFR, a computationally efficient method is introduced with compressive covariance sensing. That can be efficient implemented via only the non-uniform fast Fourier transform, fast Fourier transform, and some simple multiplication operations. Meanwhile, the state-of-the-art power spectrum reconstruction model for NYFR of time-domain and frequency-domain is constructed in this paper as a comparison. Furthermore, the computational complexity of the proposed method scales linearly with the Nyquist-rate sampled number of samples and the sparsity of spectrum occupancy. Simulation results and discussion demonstrate that the low complexity in sampling and computation is a more practical solution to meet the real-time wideband spectrum sensing applications.