Covariance-Based OFDM Spectrum Sensing with Sub-Nyquist Samples

TL;DR

This paper introduces a covariance-based spectrum sensing method for OFDM signals using sub-Nyquist samples, leveraging covariance matrix structure and GLRT for detection, applicable to frequency-selective channels.

Contribution

It presents a novel feature-based detection approach that converts underdetermined covariance equations into an overdetermined system, enabling effective OFDM spectrum sensing from sub-Nyquist samples.

Findings

Effective detection of OFDM signals from sub-Nyquist samples.

Extension of the method to frequency-selective channels.

Analysis of sample covariance matrix properties.

Abstract

In this paper, we propose a feature-based method for spectrum sensing of OFDM signals from sub-Nyquist samples over a single band. We exploit the structure of the covariance matrix of OFDM signals to convert an underdetermined set of covariance-based equations to an overdetermined one. The statistical properties of sample covariance matrix are analyzed and then based on that an approximate Generalized Likelihood Ratio Test (GLRT) for detection of OFDM signals from sub-Nyquist samples is derived. The method is also extended to the frequency-selective channels.