Open AccessHybrid cooperative spectrum sensing scheme based on spatial–temporal correlation in cognitive radio enabled VANET
Author(s) -
Li Xi,
Song Tiecheng,
Zhang Yueyue,
Chen Guojun,
Hu Jing
Publication year - 2019
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.2018.5667
Subject(s) - cognitive radio , computer science , false alarm , spatial correlation , overhead (engineering) , antenna diversity , stability (learning theory) , vehicular ad hoc network , correlation , diversity gain , wireless ad hoc network , real time computing , algorithm , artificial intelligence , machine learning , fading , decoding methods , wireless , telecommunications , mathematics , geometry , operating system
Cognitive radio enabled vehicular ad‐hoc networks (CR‐VANETs) are one of the promising architectures in the future vehicular ad‐hoc networks. In this study, the joint spatial–temporal correlation is exploited to improve decision accuracy of cooperative spectrum sensing (CSS) while reducing overhead introduced by cooperation in the CR‐VANETs. Firstly, a theoretical method is presented to analyse the impact of spatial–temporal correlation on cooperative sensing performance when using soft combining in the CR‐VANET. Then, the expression of the optimal probability of detection with respect to spatial–temporal correlation is given for a target probability of false alarm by employing likelihood ratio test. Additionally, the user selection problem is formulated as an efficient double‐threshold optimisation problem by considering both sensing accuracy and stability to achieve the optimal probability of detection. Finally, a hybrid CSS scheme based on spatial–temporal correlation is designed for the CR‐VANET. Simulation results reveal that the proposed scheme could achieve significant sensing performance gain by selecting a subset of secondary users for combination, and could reduce user selection frequency by employing spatial–temporal diversity.