Matched filter detection with dynamic threshold for cognitive radio networks

TL;DR

This paper proposes a dynamic threshold approach for matched filter spectrum sensing in cognitive radio networks, aiming to improve detection efficiency by adapting to noise variability, and compares it with existing static threshold methods.

Contribution

It introduces an estimated dynamic threshold for matched filter detection, enhancing spectrum sensing performance in cognitive radio networks compared to static threshold techniques.

Findings

Dynamic threshold improves detection accuracy.

Simulation results outperform static threshold methods.

Enhanced spectrum sensing efficiency in noisy environments.

Abstract

-In cognitive radio networks, spectrum sensing aims to detect the unused spectrum channels in order to use the radio spectrum more efficiently. Various methods have been proposed in the past, such as energy, feature detection, and matched filter. These methods are characterized by a sensing threshold, which plays an important role in the sensing performance. Most of the existing techniques used a static threshold. However, the noise is random, and, thus the threshold should be dynamic. In this paper, we suggest an approach with an estimated and dynamic sensing threshold to increase the efficiency of the sensing detection. The matched filter method with dynamic threshold is simulated and its results are compared to those of other existing techniques. Keywords-cognitive radio networks; spectrum sensing; energy detection; matched filter detection; autocorrelation based sensing; estimated dynamic threshold