Censoring for Improved Sensing Performance in Infrastructure-less Cognitive Radio Networks

TL;DR

This paper demonstrates that censoring significantly improves sensing accuracy, energy efficiency, and reduces transmission overhead in infrastructure-less cognitive radio networks using binary consensus algorithms.

Contribution

It introduces and analytically verifies the effectiveness of censoring in decentralized cognitive radio networks without a fusion center, showing substantial performance improvements.

Findings

Error probability reduced by up to 46.6%

Energy expenditure decreased by about 48.7%

Transmission overhead savings up to 50%

Abstract

Censoring has been proposed to be utilized in wireless distributed detection networks with a fusion center to enhance network performance in terms of error probability in addition to the well-established energy saving gains. In this paper, we further examine the employment of censoring in infrastructure-less cognitive radio networks, where nodes employ binary consensus algorithms to take global decisions regarding a binary hypothesis test without a fusion center to coordinate such a process. We show analytically - and verify by simulations - that censoring enhances the performance of such networks in terms of error probability and convergence times. Our protocol shows performance gains up to 46.6% in terms of average error probability over its conventional counterpart, in addition to performance gains of about 48.7% in terms of average energy expenditure and savings up to 50% in incurred transmission overhead.