• Title/Summary/Keyword: Weighted Collaborative Sensing

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An Efficient Weighted-Collaborative Sensing Scheme in Cognitive Radio

  • Huang, Xiaoge;Han, Ning;Zheng, Guanbo;Sohn, Sung-Hwan;Kim, Jae-Moung
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.32 no.10A
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    • pp.984-991
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    • 2007
  • Cognitive Radio is an advanced enabling techlology for efficient utilization of under-utilized spectrum since it is able to sense the temporally available spectrum and adapt its parameters to fully utilize the frequency band. Recent investigation suggests that spectrum sensing is compromised when a cognitive radio user suffers from the environment with fading or shadowing. In order to combat the effect, collaborative sensing is considered to be a promising way, which combines the sensing result of each user to achieve good performance. However, the conventional collaborative sensing is not efficient when users suffer different fading environments. In this paper, we propose a weighted-collaborative scheme that considers using the weights of each collaborative CR user, which can achieve better sensing performance under both fast and slow fading environments. The analysis of the simulation resultsproves that the weighted-collaborative scheme improves sensing performance obviously and outperforms the conventional method.

Improved Weighted-Collaborative Spectrum Sensing Scheme Using Clustering in the Cognitive Radio System (클러스터링 기반의 CR시스템에서 가중치 협력 스펙트럼 센싱 기술의 개선연구)

  • Choi, Gyu-Jin;Shon, Sung-Hwan;Lee, Joo-Kwan;Kim, Jae-Moung
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.7 no.6
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    • pp.101-109
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    • 2008
  • In this paper, we introduce clustering scheme to calculate probability of detection which is practically required for conventional weighted-collaborative sensing technique. We also propose an improved weighted-collaborative spectrum sensing scheme using new weight generation algorithm to achieve better performance in Cognitive Radio systems. We calculate Pd in each cluster which is a CR users group with similar channel situation. New weight factor is generated using square sum of all cluster's Pds. Simulations under slow fading show that we can get better total detection probability and lower false alarm rate when PU (Primary User) suddenly terminates their transmission.

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