Optimal periodic cooperative spectrum sensing based on weight fusion in cognitive radio networks - PubMed Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2013 Apr 19;13(4):5251-72.
doi: 10.3390/s130405251.

Optimal periodic cooperative spectrum sensing based on weight fusion in cognitive radio networks

Xin Liu  1 , Min Jia, Xuemai Gu, Xuezhi Tan
Affiliations

Optimal periodic cooperative spectrum sensing based on weight fusion in cognitive radio networks

Xin Liu et al. Sensors (Basel). .

Abstract

The performance of cooperative spectrum sensing in cognitive radio (CR) networks depends on the sensing mode, the sensing time and the number of cooperative users. In order to improve the sensing performance and reduce the interference to the primary user (PU), a periodic cooperative spectrum sensing model based on weight fusion is proposed in this paper. Moreover, the sensing period, the sensing time and the searching time are optimized, respectively. Firstly the sensing period is optimized to improve the spectrum utilization and reduce the interference, then the joint optimization algorithm of the local sensing time and the number of cooperative users, is proposed to obtain the optimal sensing time for improving the throughput of the cognitive radio user (CRU) during each period, and finally the water-filling principle is applied to optimize the searching time in order to make the CRU find an idle channel within the shortest time. The simulation results show that compared with the previous algorithms, the optimal sensing period can improve the spectrum utilization of the CRU and decrease the interference to the PU significantly, the optimal sensing time can make the CRU achieve the largest throughput, and the optimal searching time can make the CRU find an idle channel with the least time.

PubMed Disclaimer

Figures

Figure 1.
Figure 1.
Energy sensing model.
Figure 2.
Figure 2.
Cognitive radio networks.
Figure 3.
Figure 3.
Periodic cooperative sensing model.
Figure 4.
Figure 4.
Interference and loss of spectrum access during one period.
Figure 5.
Figure 5.
Sensing period including two state transitions.
Figure 6.
Figure 6.
Processes of sensing spectrum and searching channel.
Figure 7.
Figure 7.
Total sensing loss probability vs. sensing period.
Figure 8.
Figure 8.
(a) Probability of spectrum utilization vs. cooperative false alarm probability (η1=1 and η2=0.1). (b) Probability of interference vs. cooperative false alarm probability (η1=1 and η2=0.1). (c) Probability of spectrum utilization vs. cooperative false alarm probability (η1=0.1and η2=1). (d) Probability of interference vs. cooperative false alarm probability (η1=0.1and η2=1).
Figure 9.
Figure 9.
Average throughput vs. sensing time.
Figure 10.
Figure 10.
Average throughput vs. SNR.
Figure 11.
Figure 11.
Average searching time vs. average idle probability.
Figure 12.
Figure 12.
Minimal single-channel searching time vs. the number of cooperative CRUs.
Figure 13.
Figure 13.
Proportion of detected channels vs. average channel idle probability.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Krenik W., Batra A. Cognitive Radio techniques for wide area networks. Proceedings of 42nd Design Automation Conference (DAC 2005); San Diego, CA, USA. 13–17 June 2005; pp. 409–412.
    1. Mitola J., Maguire G.Q. Cognitive radio: Making software radios more personal. IEEE Pers. Commun. 1999;4:13–18.
    1. Haykin S. Cognitive radio: Brain-empowered wireless communications. IEEE J. Sel. Areas Commun. 2005;2:201–220.
    1. Shen J.Y., Liu S.Y. Soft Versus Hard Cooperative Energy Detection under Low SNR. Proceedings of the 2008 Third International Conference on Communications and Networking in China; Hangzhou, China. 25–27 August 2008; pp. 128–131.
    1. Digham F.F., Alouini M.S. On the energy detection of unknown signals over fading channels. IEEE Trans. Commun. 2007;1:21–24.

Publication types

LinkOut - more resources