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Performance of Spectrum Sharing System in Gamma Shadowed Nakagami-m Fading Environment

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Abstract

In this paper, the performance of spectrum sharing system in composite Nakagami-m/gamma shadowed channels is analyzed. The environments (of primary and secondary users) corrupted only by noise or interference are considered in details. The novel analytical expressions for outage probability, moment generation function and ergodic capacity are derived in a form of fast-computable Meijer’s G functions, for both mentioned scenarios. Numerical results illustrate the impact of fading and shadowing phenomena on system performance, and appropriate comparison between noise- and interference-limited cases is presented.

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References

  1. Goldsmith, A., Jafar, S. A., Maric, I., & Srinivasa, S. (2009). Breaking spectrum gridlock with cognitive radios: An information theoretic perspective. Proceedings of the IEEE, 97(5), 894–914.

    Article  Google Scholar 

  2. Mitola, J. (1999). Cognitive radio for flexible mobile multimedia communications. 1999 IEEE international workshop on mobile multimedia communications (MoMuC ‘99), pp. 3–10.

  3. Haykin, S. (2005). Cognitive radio: Brain-empowered wireless communications. IEEE Journal on Selected Areas in Communications, 23(2), 201–220.

    Article  Google Scholar 

  4. Ghasemi, A., & Sousa, E. S. (2008). Spectrum sensing in cognitive radio networks: Requirements, challenges and design trade-offs. IEEE Communications Magazine, 46(4), 32–39.

    Article  Google Scholar 

  5. Ghasemi, A., & Sousa, E. S. (2007). Fundamental limits of spectrum-sharing in fading environments. IEEE Transactions on Wireless Communications, 6(2), 649–658.

    Article  Google Scholar 

  6. Jovicic, A., & Viswanath, P. (2006). Cognitive radio: An information-theoretic perspective. In Proceedings IEEE international symposium on information theory, pp. 2413–2417.

  7. Kang, X., Liang, Y.-C., Nallanathan, A., Garg, H. K., & Zhang, R. (2009). Optimal power allocation for fading channels in cognitive radio networks: Ergodic capacity and outage capacity. IEEE Transactions on Wireless Communications, 8(2), 940–950.

    Article  Google Scholar 

  8. Suraweera, H. A., Smith, P. J., & Shafi, M. (2010). Capacity limits and performance analysis of cognitive radio with imperfect channel knowledge. IEEE Transactions on Vehicular Technology, 59(4), 1811–1822.

    Article  Google Scholar 

  9. Kostic, I. M. (2005). Analytical approach to performance analysis for channel subject to shadowing and fading. IEE Proceeding Communications, 152(6), 821–827.

    Article  Google Scholar 

  10. Shankar, P. M. (2011). Statistical models for fading and shadowed fading channels in wireless systems: A pedagogical perspective. Wireless Personal Communications, 60(2), 191–213.

    Article  MathSciNet  Google Scholar 

  11. Bithas, P. S., Sagias, N. C., Mathiopoulos, P. T., Karagiannidis, G. K., et al. (2006). On the performance analysis of digital communications over generalized-K fading channels. IEEE Communications Letters, 10(5), 353–355.

    Article  Google Scholar 

  12. Shankar, P. M. (2004). Error rates in generalized shadowed fading channels. Wireless Personal Communications, 28(3), 233–238.

    Article  Google Scholar 

  13. Rasheed, H., & Rajatheva, N. (2011). Spectrum sensing for cognitive vehicular networks over composite fading. International Journal of Vehicular Technology 9. Article ID 630467.

  14. Hanif, M. F., & Smith, P. J. (2010). On the statistics of cognitive radio capacity in shadowing and fast fading environments. IEEE Transactions on Wireless Communications, 9(2), 844–852.

    Article  Google Scholar 

  15. Gradshteyn, I. S., & Ryzhik, I. M. (1994). Tables of integrals, series, and products (5th ed.). New York: Academic Press.

    MATH  Google Scholar 

  16. Adamchik, V. S., & Marichev, O. I. (1990). The algorithm for calculating integrals of hypergeometric type functions and its realization in reduce system. In Proceedings of international symposium on symbolic and algebraic computation, Tokyo, Japan, pp. 212–224.

  17. Prudnikov, A. P., Brychkov, Y. A., & Marichev, O. I. (2003). Integrals and series’, part 3, more special functions (2nd ed.). Moscow: Fizmatlit.

    MATH  Google Scholar 

  18. Helstrom, C. W. (1991). Probability and stochastic processes for engineers (2nd ed.). New York: MakMillian.

    Google Scholar 

Download references

Acknowledgments

This paper was supported by the Ministry of Science of Republic of Serbia under Grants TR-32028 and III-44006.

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Correspondence to Jelena A. Anastasov.

Appendix

Appendix

The PDF of the RV z defined as \(z = x_{1} /x_{2}\) can be evaluated using the method given in [18] as

$$p_{z} \left( z \right) = \int\limits_{0}^{\infty } {x_{2} p_{{x_{1} }} \left( {zx_{2} } \right)p_{{x_{2} }} \left( {x_{2} } \right)dx_{2} } .$$
(18)

Considering the PDFs given in a form of (4) and representing K k m (.) by Meijer’s G function as \(K_{\nu } \left( x \right) = \frac{1}{2}G_{0,2}^{2,0} \left( {\frac{{x^{2} }}{4}\left| \begin{aligned} & \_\hfill \\ & \frac{\nu }{2}, - \frac{\nu }{2} \hfill \\ \end{aligned} \right.} \right)\), [17, Eq. (8.4.23.1)], (18) becomes

$$\begin{aligned} p_{z} \left( z \right) & = \left( {\frac{{m_{1} k_{1} }}{{\varOmega_{1} }}} \right)^{{\frac{{m_{1} + k_{1} }}{2}}} \left( {\frac{{m_{2} k_{2} }}{{\varOmega_{2} }}} \right)^{{\frac{{m_{2} + k_{2} }}{2}}} \frac{{z^{{\frac{{m_{1} + k_{1} }}{2} - 1}} }}{{\varGamma \left( {m_{1} } \right)\varGamma \left( {k_{1} } \right)\varGamma \left( {m_{2} } \right)\varGamma \left( {k_{2} } \right)}} \\ & \quad \times \int\limits_{0}^{\infty } {x_{2}^{{\frac{{m_{1} + k_{1} + m_{2} + k_{2} }}{2} - 1}} } G_{0,2}^{2,0} \left( {\frac{{zx_{2} m_{1} k_{1} }}{{\varOmega_{1} }}\left| \begin{aligned} & - \\ & \frac{{k_{1} - m_{1} }}{2}, - \frac{{k_{1} - m_{1} }}{2} \\ \end{aligned} \right.} \right)G_{0,2}^{2,0} \left( {\frac{{x_{2} m_{2} k_{2} }}{{\varOmega_{2} }}\left| \begin{aligned} & - \\ & \frac{{k_{2} - m_{2} }}{2}, - \frac{{k_{2} - m_{2} }}{2} \\ \end{aligned} \right.} \right)dx_{2} . \\ \end{aligned}$$
(19)

Integral in (19) can be solved in the exact closed-form, using the procedure of integrating product of two Meijer’s G functions [17, Eq. (2.24.1.1)], in a way

$$\begin{aligned} p_{z} \left( z \right) & = z^{{\frac{{m_{1} + k_{1} }}{2} - 1}} \left( {\frac{{m_{1} k_{1} \varOmega_{2} }}{{m_{2} k_{2} \varOmega_{1} }}} \right)^{{\frac{{m_{1} + k_{1} }}{2}}} \frac{1}{{\varGamma \left( {m_{1} } \right)\varGamma \left( {k_{1} } \right)\varGamma \left( {m_{2} } \right)\varGamma \left( {k_{2} } \right)}} \\ & \quad \times G_{2,2}^{2,2} \left( {\frac{{m_{1} k_{1} \varOmega_{2} z}}{{m_{2} k_{2} \varOmega_{1} }}\left| \begin{aligned} & 1 - \frac{{m_{1} + k_{1} + 2k_{2} }}{2},1 - \frac{{m_{1} + k_{1} + 2m_{2} }}{2} \\ & \frac{{k_{1} - m_{1} }}{2}, - \frac{{k_{1} - m_{1} }}{2} \\ \end{aligned} \right.} \right). \\ \end{aligned}$$
(20)

Furthermore, to obtain the PDF of RV \(\gamma^{NL} = c_{1} z\) we use the transformation \(p_{\gamma }^{NL} \left( \gamma \right) = \frac{{p_{z} \left( z \right)}}{{\frac{d\gamma }{{dz}}}}\left| \begin{aligned} \hfill \\ z = \frac{\gamma }{{c_{1} }} \hfill \\ \end{aligned} \right.\) [18], and the PDF of equivalent SNR is derived in the form of (6).

Considering the same procedure as for deriving the PDF p z (z), the PDF of the RV y = z/x 3, can be also obtained. Substituting (20) into \(p_{y} \left( y \right) = \int\limits_{0}^{\infty } {x_{3} p_{z} \left( {yx_{3} } \right)p_{{x_{3} }} \left( {x_{3} } \right)dx_{3} }\), with the help of [17, Eqs. (8.4.23.1), (2.24.1.1)] we get

$$\begin{aligned} p_{y}^{IL} \left( y \right) & = \frac{1}{{\varGamma \left( {m_{1} } \right)\varGamma \left( {k_{1} } \right)\varGamma \left( {m_{2} } \right)\varGamma \left( {k_{2} } \right)\varGamma \left( {m_{3} } \right)\varGamma \left( {k_{3} } \right)}}y^{{\frac{{m_{1} + k_{1} }}{2} - 1}} \\ & \quad \times G_{4,2}^{2,4} \left( {\frac{{m_{1} k_{1} \varOmega_{2} \varOmega_{3} y}}{{m_{2} k_{2} m_{3} k_{3} \varOmega_{1} }}\left| \begin{aligned} & 1 - \frac{{m_{1} + k_{1} + 2k_{2} }}{2},1 - \frac{{m_{1} + k_{1} + 2m_{2} }}{2},\chi_{2} \\ & \frac{{k_{1} - m_{1} }}{2}, - \frac{{k_{1} - m_{1} }}{2} \\ \end{aligned} \right.} \right). \\ \end{aligned}$$
(21)

Finally, the PDF of the RV \(\gamma^{IL} = c_{2} y\) is found, relating to transformation \(p_{\gamma }^{IL} \left( \gamma \right) = \frac{{p_{y} \left( y \right)}}{{\frac{d\gamma }{{dy}}}}\left| \begin{aligned} \hfill \\ y = \frac{\gamma }{{c_{2} }} \hfill \\ \end{aligned} \right.\) [18], in a form given in (8).

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Anastasov, J.A., Blagojevic, V.M., Ivanis, P.N. et al. Performance of Spectrum Sharing System in Gamma Shadowed Nakagami-m Fading Environment. Wireless Pers Commun 86, 1717–1729 (2016). https://doi.org/10.1007/s11277-015-3015-9

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