Hard Fusion Based Spectrum Sensing over Mobile Fading Channels in Cognitive Vehicular Networks
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Key Lab of Information Coding and Transmission, Southwest Jiaotong University, Chengdu 610031, China
2
Faculty of Electrical and Electronic Engineering, University of Transport and Communications, Hanoi 117262, Vietnam
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(2), 475; https://doi.org/10.3390/s18020475
Received: 24 December 2017
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Revised: 2 February 2018
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Accepted: 2 February 2018
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Published: 6 February 2018
(This article belongs to the Special Issue Smart Vehicular Mobile Sensing)
An explosive growth in vehicular wireless applications gives rise to spectrum resource starvation. Cognitive radio has been used in vehicular networks to mitigate the impending spectrum starvation problem by allowing vehicles to fully exploit spectrum opportunities unoccupied by licensed users. Efficient and effective detection of licensed user is a critical issue to realize cognitive radio applications. However, spectrum sensing in vehicular environments is a very challenging task due to vehicle mobility. For instance, vehicle mobility has a large effect on the wireless channel, thereby impacting the detection performance of spectrum sensing. Thus, gargantuan efforts have been made in order to analyze the fading properties of mobile radio channel in vehicular environments. Indeed, numerous studies have demonstrated that the wireless channel in vehicular environments can be characterized by a temporally correlated Rayleigh fading. In this paper, we focus on energy detection for spectrum sensing and a counting rule for cooperative sensing based on Neyman-Pearson criteria. Further, we go into the effect of the sensing and reporting channel conditions on the sensing performance under the temporally correlated Rayleigh channel. For local and cooperative sensing, we derive some alternative expressions for the average probability of misdetection. The pertinent numerical and simulating results are provided to further validate our theoretical analyses under a variety of scenarios.
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Keywords:
cognitive radio; cognitive vehicular networks; spectrum sensing; sensing/reporting channel; correlated rayleigh fading channel; hard fusion
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MDPI and ACS Style
Qian, X.; Hao, L.; Ni, D.; Tran, Q.T. Hard Fusion Based Spectrum Sensing over Mobile Fading Channels in Cognitive Vehicular Networks. Sensors 2018, 18, 475. https://doi.org/10.3390/s18020475
AMA Style
Qian X, Hao L, Ni D, Tran QT. Hard Fusion Based Spectrum Sensing over Mobile Fading Channels in Cognitive Vehicular Networks. Sensors. 2018; 18(2):475. https://doi.org/10.3390/s18020475
Chicago/Turabian StyleQian, Xiaomin, Li Hao, Dadong Ni, and Quang T. Tran. 2018. "Hard Fusion Based Spectrum Sensing over Mobile Fading Channels in Cognitive Vehicular Networks" Sensors 18, no. 2: 475. https://doi.org/10.3390/s18020475
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