Improving Throughput in Sub – Nyquist Spectrum Sensing using a Cluster Model for Cognitive Radio Networks

Abstract

Abstract The underutilization of radio spectrum has led to the downfall of Fixed Spectrum Allocation (FSA) since mobile frequency bands are getting overloaded due to rise of users all across the world. These drawbacks could be overcome by a concept called Dynamic Spectrum Allocation (DSA) implemented by an intelligent form of communication termed as Cognitive Radio Networks (CRN). CRN finds its applications adopted in military for battlefield surveillance, medical areas for 24 × 7 patient monitoring and also in TV white spaces. CRN involves SS in an efficient manner to deploy mobile users in TV frequency bands in a wider range through Wideband SS (WSS). WSS provides omnipresent spectrum monitoring for detecting spectral holes in the unutilized TV spectrum. The concept of Sub-Nyquist Sensing in WSS has several advantages in data sampling improving a plethora of application throughputs. The cluster approach in CRN has improved the base station crisis in load handling, throughput performance. In this paper, we focus on improving the traffic Quality of service (QoS) of the users in WSS by employing a cluster CRN network and applying Nyquist Sensing (NS) and Sub-Nyquist sensing (SNS) to find out the significance of SNS in the cluster network.