Spectrum Sensing with Small-Sized Datasets in Cognitive Radio: Algorithms and Analysis

TL;DR

This paper introduces a new spectrum sensing method for cognitive radio that effectively detects primary users using small-sized datasets, supported by theoretical analysis and simulation results.

Contribution

It proposes a cumulative spectrum sensing algorithm with a novel covariance matrix estimation technique, suitable for real-time detection with limited data.

Findings

Operates effectively with smaller data sizes

Performs well under low SNR conditions

Outperforms traditional methods in simulations

Abstract

Spectrum sensing is a fundamental component of cognitive radio. How to promptly sense the presence of primary users is a key issue to a cognitive radio network. The time requirement is critical in that violating it will cause harmful interference to the primary user, leading to a system-wide failure. The motivation of our work is to provide an effective spectrum sensing method to detect primary users as soon as possible. In the language of streaming based real-time data processing, short-time means small-sized data. In this paper, we propose a cumulative spectrum sensing method dealing with limited sized data. A novel method of covariance matrix estimation is utilized to approximate the true covariance matrix. The theoretical analysis is derived based on concentration inequalities and random matrix theory to support the claims of detection performance. Comparisons between the proposed method and other traditional approaches, judged by the simulation using a captured digital TV signal, show that this proposed method can operate either using smaller-sized data or working under lower SNR environment.