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A novel interference modeling scheme in cognitive networks

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Abstract

In this paper, we propose a mathematical model of aggregate co-channel interference over Rayleigh fading in cognitive networks. Unlike the statistical models in the literature that aim at finding the bound or approximation of the interference, the proposed model gives an accurate expression of probability density function (PDF), cumulative distribution function (CDF) and mean and variance of the interference, which takes into account a number of factors, such as spectrum sensing scheme, and spatial distribution of the secondary users (SUs). In particular, we focus on a more general spatial structure where there are two roles of primary users (PUs) and the interfering SUs distributed in the two-dimensional space. The framework developed in this paper is easy to be applied in power control, error evaluation and other applications.

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References

  1. Ghasemi A, Sousa E S. Interference aggregation in spectrum-sensing cognitive wireless networks [J]. IEEE Journal of Selected Topics in Signal Processing, 2008, 2(1): 41–56.

    Article  Google Scholar 

  2. Rabbachin A, Quek T Q S, Shin H, et al. Cognitive network interference [J]. IEEE Journal on Selected Areas in Communications, 2011, 29(2): 480–493.

    Article  Google Scholar 

  3. Chen Z M, Wang C X, Hong X M, et al. Aggregate interference modeling in cognitive radio networks with power and contention control [J]. IEEE Transaction on Communications, 2012, 60(2): 456–468.

    Article  Google Scholar 

  4. Haenggi M, Ganti R K. Interference in large wireless networks [J]. Foundations and Trends in Networking, 2008, 3(2): 127–248.

    Article  MATH  Google Scholar 

  5. Win M Z, Pinto P C, Shepp L A. A mathematical theory of network interference and its applications [J]. Proceedings of the IEEE, 2009, 97(2): 205–230.

    Article  Google Scholar 

  6. Dahama R, Sowerby K W, Rowe G B. Outage probability estimation for licensed systems in the presence of cognitive radio interference [C]//in Proceedings of the IEEE 69th Vehicular Technology Conference (VETECS’ 09). Barcelona: IEEE, 2009: 1–5.

    Google Scholar 

  7. Salbaroli E, Zanella A. Interference analysis in a Poisson field of nodes of finite area [J]. IEEE Transaction on Vehicular Technology, 2009, 58(4): 1176–1783.

    Article  Google Scholar 

  8. Vijayandran L, Dharmawansa P, Ekman T, et al. Analysis of aggregate interference and primary system performance in finite area cognitive radio networks [J]. IEEE Transaction on Communications, 2012, 60(7): 1811–1822.

    Article  Google Scholar 

  9. Kusaladharma S, Tellambura C. Aggregate interference analysis for underlay cognitive radio networks [J]. IEEE Wireless Communications Letters, 2012, 1(6): 641–644.

    Article  Google Scholar 

  10. Pinto P C, Win M Z. Communication in a Poisson field of interferers. Part I. Interference distribution and error probability [J]. IEEE Transaction on Wireless Communications, 2010, 9(7): 2176–2186.

    Article  Google Scholar 

  11. Lee C H, Haenggi M. Interference and outage in Poisson cognitive networks [J]. IEEE Transaction on Wireless Communications, 2012, 11(4): 1392–1401.

    Article  Google Scholar 

  12. Stüber G L. Principles of mobile communication [M]. New York: KLUWER, 2002: 39–152.

    Google Scholar 

  13. Li J, Li S H. Interference of cognitive wireless networks on Rayleigh and rice fading channels [J]. International Journal of Distributed Sensor Networks, 2014. DOI: 10.1155/2014/236891 (published online).

    Google Scholar 

  14. Kingman J F C. Poisson processes [M]. New York: Oxford University Press, 1993.

    Google Scholar 

  15. Moghimi F, Nasri A, Schober R. Adaptive Lpnorm spectrum sensing for cognitive radio networks [J]. IEEE Transaction on Communications, 2011, 59(7): 1934–1945.

    Article  Google Scholar 

  16. Wen Y B, Loyka S, Yongacoglu A. Asymptotic analysis of interference in cognitive radio networks [J]. IEEE Journal on Selected Areas in Communications, 2012, 30(10): 2040–2052.

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Electronic Engineering, Shanghai Jiaotong University, Shanghai, 200240, China

    Jian Li  (李剑) & Sheng-hong Li  (李生红)

  2. The Third Research Institute of Ministry of Public Security of P.R.C, Shanghai, 200031, China

    Jian Li  (李剑)

Authors
  1. Jian Li  (李剑)
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  2. Sheng-hong Li  (李生红)
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Corresponding author

Correspondence to Jian Li  (李剑).

Additional information

Foundation item: the National Natural Science Foundation of China (Nos. 61071152 and 61271316), the National Basic Research Program (973) of China (Nos. 2010CB731406 and 2013CB329605) and the National “Twelfth Five-Year” Plan for Science & Technology Support (No. 2012BAH38B04)

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Li, J., Li, Sh. A novel interference modeling scheme in cognitive networks. J. Shanghai Jiaotong Univ. (Sci.) 20, 540–547 (2015). https://doi.org/10.1007/s12204-015-1661-4

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  • DOI: https://doi.org/10.1007/s12204-015-1661-4

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