Open AccessA new joint eigenvalue distribution of finite random matrix for cognitive radio networks
Author(s) -
Zhang Wensheng,
Sun Jian,
Xiong Hailiang,
Chen Di
Publication year - 2016
Publication title -
iet communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.355
H-Index - 62
eISSN - 1751-8636
pISSN - 1751-8628
DOI - 10.1049/iet-com.2015.0869
Subject(s) - random matrix , cognitive radio , joint (building) , eigenvalues and eigenvectors , joint probability distribution , computer science , mathematics , telecommunications , statistics , wireless , physics , engineering , quantum mechanics , architectural engineering
A new joint eigenvalue distribution (JED) based on dual extreme eigenvalues of finite random matrix is proposed in this study. Different from conventional JED based on K ( K ≥ 2) variables, only two variables are included in the proposed formulation. The upper and lower bounds of the new JED are determined. The new JED provides a simple and efficient way to deduce the distributions of key characteristics of finite random matrix, such as the extreme (largest and smallest) eigenvalues, standard condition number, and scaled largest eigenvalue. Moreover, a novel cooperative spectrum sensing (CSS) scheme based on the new JED is proposed for cognitive radio networks. The simulation results verify the proposed JED and the proposed CSS scheme can improve sensing performance.