Stochastic Approach Optimization Algorithm for Cognitive Radio Spectrum Sensing Estimation Delay Time

JIANG Xianyang; XIA Kaili; TANG Xianghong

doi:10.11999/JEIT170122

Volume 39 Issue 11

Nov. 2017

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JIANG Xianyang, XIA Kaili, TANG Xianghong. Stochastic Approach Optimization Algorithm for Cognitive Radio Spectrum Sensing Estimation Delay Time[J]. Journal of Electronics & Information Technology, 2017, 39(11): 2548-2555. doi: 10.11999/JEIT170122

Citation:

JIANG Xianyang, XIA Kaili, TANG Xianghong. Stochastic Approach Optimization Algorithm for Cognitive Radio Spectrum Sensing Estimation Delay Time[J]. Journal of Electronics & Information Technology, 2017, 39(11): 2548-2555. doi: 10.11999/JEIT170122

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Stochastic Approach Optimization Algorithm for Cognitive Radio Spectrum Sensing Estimation Delay Time

doi: 10.11999/JEIT170122

Funds:

Zhejiang Province Science and Technology Plan Public Welfare Technology Application Research Project (2017C31055), 2017 Open Research Foundation of Electronics Science and Technology for Top-ranking Discipline A Class in Zhejiang Province

Received Date: 2017-02-15
Rev Recd Date: 2017-08-17
Publish Date: 2017-11-19

Abstract

Abstract

In order to improve the spectrum efficiency in the cognitive radio networks, the optimization algorithm of the spectrum sensing estimation time is presented. The longer sensing time will bring two aspects of the consequences. On the one hand, the channel parameters are estimated more accurate so as to reduce the interference to the authorized users and to improve the throughput of the cognitive users. On the other hand, it shortens the transmission time so as to decease the system throughput. In this time, it exists an optimal sensing time to maximize the throughput. It is considered that the channel state information of sub-bands is exponentially distributed, so a stochastic programming method is proposed to optimize the sensing time of the cognitive radio networks. The computer simulation results show that the algorithm is effective and has a certain engineering application value.
- Cognitive radio networks,
- Spectrum sensing,
- Stochastic approach

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References(18)

References

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