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Copyright © 2011 Haroon Rasheed and Nandana Rajatheva. Haroon Rasheed et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Recent advancement in vehicular wireless applications is also a major contributing factor in spectrum scarcity. Cognitive radio system is a mechanism which allows unlicensed cognitive users (CUs) to utilize idle unused bands. Fast and reliable detection of primary legacy user is the key component of cognitive radio networks. However, hidden terminal and low SNR problems due to shadow fading put fundamental limit to the sensing performance and practical entailments in design of the cognitive vehicular networks. Extensive modeling is being carried out to specify varying channel characteristics, particularly multipath fading and shadowing. Energy detection-(ED-) based spectrum sensing is a viable choice for many vehicle-to-vehicle (V2V) and vehicle to-road-side infrastructure (V2I) communications. This paper examines the performance of spectrum sensing using ED over Gamma-shadowed Nakagami-m composite fading channel to cater for both small-and-large scale fading. The results highlight the notable impact of shadowing spread and fading severity on detection performance. The relevant simulation results are presented to support our analytical results for average detection probability. Furthermore, these results are investigated and compared to other compound and classical channels.

Details

Title
Spectrum Sensing for Cognitive Vehicular Networks over Composite Fading
Author
Rasheed, Haroon; Rajatheva, Nandana
Pages
n/a
Publication year
2011
Publication date
2011
Publisher
Hindawi Limited
ISSN
16875702
e-ISSN
16875710
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
863252594
Copyright
Copyright © 2011 Haroon Rasheed and Nandana Rajatheva. Haroon Rasheed et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.