Real-time Distributed Cooperative Spectrum DetectionAlgorithm Based on Diffusion Strategy

Zhang Zheng-bao; Yao Shao-lin; Xu Xin; Liu Guang-kai

doi:10.11999/JEIT150460

Volume 37 Issue 12

Jan. 2016

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Article Navigation > Journal of Electronics & Information Technology > 2015 > 37(12): 2858-2865

Zhang Zheng-bao, Yao Shao-lin, Xu Xin, Liu Guang-kai. Real-time Distributed Cooperative Spectrum DetectionAlgorithm Based on Diffusion Strategy[J]. Journal of Electronics & Information Technology, 2015, 37(12): 2858-2865. doi: 10.11999/JEIT150460

Citation:

Zhang Zheng-bao, Yao Shao-lin, Xu Xin, Liu Guang-kai. Real-time Distributed Cooperative Spectrum DetectionAlgorithm Based on Diffusion Strategy[J]. Journal of Electronics & Information Technology, 2015, 37(12): 2858-2865. doi: 10.11999/JEIT150460

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Real-time Distributed Cooperative Spectrum DetectionAlgorithm Based on Diffusion Strategy

doi: 10.11999/JEIT150460

Received Date: 2015-04-22
Rev Recd Date: 2015-07-03
Publish Date: 2015-12-19

Abstract

Abstract

Considering the problem of real-time distributed cooperative spectrum detection of cognitive users, a real-time distributed cooperative spectrum detection algorithm based on diffusion strategy is proposed. Global cost function can be approximated by an alternative localized cost that is amenable to distributed optimization. Each individual node optimizes this alternative cost via a steep-descent procedure that relies solely on interaction within the neighborhood of the node. The local estimate value can be calculated via the iteration procedure. A general model for analyzing the mean and variance of the estimates of the diffusion strategy is derived. The formulas of probability of detection, probability of false alarm and detection threshold are derived. Theoretical analysis and experimental results show that the proposed algorithm can effectively solve the problem of real-time detection signal, can quickly learn and adapt to environmental changes. Compared with average consensus strategy and non-real-time diffusion strategy, the average SNR of the proposed algorithm reduces about 6 dB, while the Pfa below 0.01 and Pd reached to 0.9. The diffusion strategy can satisfy the signal detection in very low SNR.
- Cognitive radio,
- Cooperative spectrum sensing,
- Distributed estimation,
- Diffusion strategy,
- Consensus strategy

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References(16)

References

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