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Volume 36 Issue 5
Jun.  2014
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Lisheng YIN, Shengqi TANG, Sheng LI, Yigang HE. Traffic Flow Prediction Based on Hybrid Model of Auto-Regressive Integrated Moving Average and Genetic Particle Swarm Optimization Wavelet Neural Network[J]. Journal of Electronics & Information Technology, 2019, 41(9): 2273-2279. doi: 10.11999/JEIT181073
Citation: Luan Hong-Zhi, Li Ou. A High-efficiency Framework for Cooperative Spectrum Sensing[J]. Journal of Electronics & Information Technology, 2014, 36(5): 1158-1163. doi: 10.3724/SP.J.1146.2013.00925

A High-efficiency Framework for Cooperative Spectrum Sensing

doi: 10.3724/SP.J.1146.2013.00925
  • Received Date: 2013-07-01
  • Rev Recd Date: 2013-11-01
  • Publish Date: 2014-05-19
  • To utilize effectively the reporting slots in Cooperative Spectrum Sensing (CSS) and enhance the sensing performance, a novel CSS framework is designed. The core idea of the proposed framework is that, when a Secondary User (SU) is reporting the sensing results, the following SUs continue local sensing until their turns to report. Since a half of idle reporting slots are appropriately utilized for sensing, the proposed framework behaves more efficiently. Moreover, based on Neyman-Pearson criteria, the optimal schedules for AND rule and OR rule are investigated respectively, followed by the discussions of the sensing performance gains under the proposed framework. Simulation results demonstrate that, the proposed framework enhances the CSS performance considerately without any additional sensing delay.
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      沈阳化工大学材料科学与工程学院 沈阳 110142

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