Efficient Centralized Cooperative Spectrum Sensing Techniques for Cognitive Networks

Abstract

Wireless Communication is a system for communicating information from one point to other, without utilizing any connections like wire, cable, or other physical medium. Cognitive Radio (CR) based systems and networks are a revolutionary new perception in wireless communications. Spectrum sensing is a vital task of CR to avert destructive intrusion with licensed primary or main users and discover the accessible spectrum for the efficient utilization of the spectrum. Centralized Cooperative Spectrum Sensing (CSS) is a kind of spectrum sensing. Most of the test metrics designed till now for sensing the spectrum is produced by using the Sample Covariance Matrix (SCM) of the received signal. Some of the methods that use the SCM for the process of detection are Pietra-Ricci Index Detector (PRIDe), Hadamard Ratio (HR) detector, Gini Index Detector (GID), etc. This paper presents the simulation and comparative performance analysis of PRIDe with various other detectors like GID, HR, Arithmetic to Geometric Mean (AGM), Volume-based Detector number 1 (VD1), Maximumto-Minimum Eigenvalue Detection (MMED), and Generalized Likelihood Ratio Test (GLRT) using the MATLAB software. The PRIDe provides better performance in the presence of variations in the power of the signal and the noise power with less computational complexity.

Reorganisation and Construction of an Age-Friendly Smart Recreational Home System: Based on Function–Capability Match Methodology

“…In 1943, Abraham H. Maslow and others put forward the capability-demand theory, which also follows the information processing model in classifying the capabilities of users [28]. The relationship between the product and the user is a two-way one, where the user's actual capabilities determine the requirements for the product itself, and the product's capabilities are adapted to the user's needs [29,30]. In such secondary iterations, information often needs to be recovered.…”

Optimized Statistical Beamforming for Cooperative Spectrum Sensing in Cognitive Radio Networks

“…Cooperative spectrum sensing (CSS) has been suggested as a potential remedy to lessen these effects by increasing the detection rate by making use of spatial diversity via cooperation among multiple sensing nodes [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35]. These include various techniques, such as distributed compressive SS for cooperative multihop CRN [24], wideband SS on real-time signal via sub-Nyquist sampling [25], CSS for energy-harvesting CRN [26], centralized CSS for CRN [27], semi-supervised deep-learning-network-based CSS [28], deep-reinforcementlearning-based decentralized CSS [29], and various machine-learning-based CSS [30][31][32][33][34][35].…”