Hard Decision Cooperative Spectrum Sensing Based on Estimating the Noise Uncertainty Factor

TL;DR

This paper introduces a noise uncertainty-aware hard decision cooperative spectrum sensing algorithm that dynamically adjusts thresholds to improve detection accuracy in cognitive radio systems, validated through theoretical and simulation analyses.

Contribution

It proposes a novel CSS algorithm with dynamic thresholds based on noise uncertainty estimation, enhancing detection reliability over conventional methods.

Findings

Improved detection probability over traditional CSS.

Reduced false alarm rate due to noise uncertainty compensation.

Validated effectiveness through theoretical and simulation results.

Abstract

Spectrum Sensing (SS) is one of the most challenging issues in Cognitive Radio (CR) systems. Cooperative Spectrum Sensing (CSS) is proposed to enhance the detection reliability of a Primary User (PU) in fading environments. In this paper, we propose a hard decision based CSS algorithm using energy detection with taking into account the noise uncertainty effect. In the proposed algorithm, two dynamic thresholds are toggled based on predicting the current PU activity, which can be successfully expected using a simple successive averaging process with time. Also, their values are evaluated using an estimated value of the noise uncertainty factor. These dynamic thresholds are used to compensate the noise uncertainty effect and increase (decrease) the probability of detection (false alarm), respectively. Theoretical analysis is performed on the proposed algorithm to deduce its enhanced false alarm and detection probabilities compared to the conventional hard decision CSS. Moreover, simulation analysis is used to confirm the theoretical claims and prove the high performance of the proposed scheme compared to the conventional CSS using different fusion rules.