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Cognitive information metrics for cognitive wireless networks

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Chinese Science Bulletin

Abstract

Considering the dynamic changes and uncertainty features of the radio environment in cognitive wireless networks (CWNs), the environment cognition ability is critical for the performance evaluation of CWNs design and optimization. However, there are no effective metrics to evaluate the ability and gain of information cognition in CWNs from an information theory perspective. Therefore, the novel cognitive information concept is proposed and defined as a metric to evaluate the uncertainty of both the internal and external environments of one system that can be removed by other systems or nodes using cognitive radio techniques. As an intelligent wireless communication system that is aware of its surrounding radio, network, and user multi-domains environment, the more cognitive information it achieves, the higher level cognitive capability it is. In this paper, we define and analyze the mathematical features of cognitive information. Results reveal that the increase of cognitive information can improve the spectrum efficiency and reduce the interference probability simultaneously in CWNs. Thus cognitive information can be regarded as a metric for CWNs optimization. Finally, we apply the theory of cognitive information in the parameters optimization in energy detection and cooperative spectrum sensing.

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References

  1. Mitola J (1999) Cognitive radio: making software radios more personal. IEEE Pers Commun 6:13–18

    Article  Google Scholar 

  2. Wang BB, Liu KJR (2011) Advances in cognitive radio networks: a survey. IEEE J Sel Top Sign Process 5:5–23

    Article  Google Scholar 

  3. Demestichas P, Dimitrakopoulos G, Strassner J et al (2006) Introducing reconfigurability and cognitive networks concepts in the wireless world. IEEE Veh Technol Mag 1:32–39

    Article  Google Scholar 

  4. Zhang P, Liu Y, Feng ZY et al (2012) Intelligent and efficient development of wireless networks: a review of cognitive radio networks. Chin Sci Bull 57:3662–3676

    Article  Google Scholar 

  5. Haykin S (2005) Cognitive radio: brain-empowered wireless communications. IEEE J Sel Areas Commun 23:201–220

    Article  Google Scholar 

  6. Zhao Y, Mao S, Neel JO et al (2009) Performance evaluation of cognitive radios: metrics, utility functions, and methodology. Proc IEEE 97:642–659

    Article  Google Scholar 

  7. Ahmed W, Gao J, Faulkner M (2009) Performance evaluation of a cognitive radio network with exponential and truncated usage models. In: Proceedings of 4th international symposium on wireless pervasive computing, Melbourne. IEEE, Washington DC, pp 1–5

  8. Cavdar D, Yilmaz H B, Tugcu T et al (2010) Analytical modeling and performance evaluation of cognitive radio networks. In: Proceedings of sixth advanced international conference on telecommunications, Barcelona. IEEE, Washington DC, pp 35–40

  9. Yucek T, Arslan H (2009) A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Commun Surv Tutor 11:116–130

    Article  Google Scholar 

  10. Liang YC, Zeng YH, Peh ECY et al (2008) Sensing-throughput tradeoff for cognitive radio networks. IEEE Trans Wireless Commun 7:1326–1337

    Article  Google Scholar 

  11. Shen J, Liu S, Zeng L et al (2009) Optimisation of cooperative spectrum sensing in cognitive radio network. IET Commun 3:1170–1178

    Article  Google Scholar 

  12. Cover TM, Thomas JA (1991) Elements of Information Theory. Wiley, New York

    Book  Google Scholar 

Download references

Acknowledgments

This work was supported by the National Natural Science Foundation of China (61227801, 61201152, 61121001), the Major State Basic Research Development Program of China (973 Program, 2009CB320400), the National Science and Technology Major Project (2012ZX03003006), the Program for New Century Excellent Talents in University (NCET-01-0259), the Fundamental Research Funds for the Central Universities (2013RC0106).

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Correspondence to Qixun Zhang or Ping Zhang.

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Feng, Z., Wei, Z., Zhang, Q. et al. Cognitive information metrics for cognitive wireless networks. Chin. Sci. Bull. 59, 2057–2064 (2014). https://doi.org/10.1007/s11434-014-0240-7

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  • DOI: https://doi.org/10.1007/s11434-014-0240-7

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