Skip to main content
Log in

Capacity in fading environment based on soft sensing information under spectrum sharing constraints

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

In this paper, the ergodic channel capacity for a secondary user is investigated using soft sensing information about primary user activity in a shared channel under joint peak transmit power and average received interference power constraints for Nakagami-m fading channel. The results of the proposed power adaptation scheme illustrate the effect of communication environment parameters and soft sensing information about primary user activity on the channel capacity of secondary user. In particular, the effect of cross link channel state information to maximize the channel capacity for the power adaptation scheme is emphasized by considering the Lagrangian optimization problem for joint peak transmit power and average interference power constraints. Moreover, the performance of the primary user is also investigated considering the interference of the secondary user to the primary in spectrum sharing environment in terms of transmission rate and average channel capacity.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Akyildiz, I. F., Lee, W. Y., Vuran, M. C., & Mohanty, S. (2006). NeXt generation/dynamic spectrum access/cognitive radio wireless networks: a survey. Computer Networks, 50(13), 2127–2159.

    Article  MATH  Google Scholar 

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

    Article  Google Scholar 

  3. Mitola, J., & Maguire, G. Q. (1999). Cognitive radio: Making software radio more personal. IEEE Personal Communication, 6(4), 13–18.

    Article  Google Scholar 

  4. Goldsmith, A. J., & Varaiya, P. (1997). Capacity of fading channels with channel side information. IEEE Transactions on Information Theory, 43(6), 1986–1992.

    Article  MathSciNet  MATH  Google Scholar 

  5. Khojastepour, M. A. & Aazhang B. (2004). The capacity of average and peak power constrained fading channels with channel side information. In Proceedings of IEEE wireless communication and networking conference (WCNC’04), Atlanta, CA, USA, pp. 77–82.

  6. Ghasemi A. & Sousa E. S. (2006). Capacity of fading channels under spectrum-sharing constraints. In IEEE international conference on computer communications, (ICC’06), Istanbul, Turkey, pp. 4373–4378.

  7. Musavian L. & Aissa S. (2007). Ergodic and outage capacities of spectrum sharing systems in fading channels. In Proceedings IEEE global telecommunications confernce (GLOBECOM’07), Washington, DC, USA, pp. 3327–31.

  8. Gastpar M. (2004). On capacity under received-signal constraints. In Proceedings 42nd annual allerton confernce on communication control and computing, Monticello,IL, USA, pp. 1322–1331.

  9. Zhang R. (2008). Optimal power control over fading cognitive radio channel by exploiting primary user CSI. In Proceedings of IEEE global telecommunication conference (GLOBECOM’08), Washington, DC, USA, pp. 1–5.

  10. Bala I., Bhamrah M. S., Rana V., Jain N. & Singh G. (2014). Adaptive power control scheme for the cognitive radio system based on receiver sensitivity. In computational advancement in communication circuits and systems (pp. 69–80), India, Springer.

  11. Asghari V. & Aissa S (2008). Resource sharing in cognitive radio systems: Outage capacity and power allocation under soft sensing. In Proceedings of the IEEE global telecommunications conference (GLOBECOM’08), New Orleans, LA, USA, pp. 1–5.

  12. Hamdi K., Zhang W, & Letaief K. B. (2007). Power control in cognitive radio systems based on spectrum sensing side information. In Proceedings of the IEEE international conference on communication (ICC’07), pp. 5161–5165.

  13. Urkowitz, H. (1967). Energy detection of unknown deterministic signals. Proceedings of the IEEE, 55(4), 523–531.

    Article  Google Scholar 

  14. Yucek, T., & Arslan, H. (2009). A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Communications Surveys and Tutorials, 11(1), 116–130.

    Article  Google Scholar 

  15. Srinivasa S. and Jafar S. A. (2007). Soft sensing and optimal power control for cognitive radio. In Proceedings of the IEEE Global telecommunications conference (GLOBECOM’07), Washington, DC, USA, pp. 1380–1384.

  16. Musavein, L., & Aissa, S. (2009). Fundamental capacity limits of cognitive radio in fading environments with imperfect channel information. IEEE Transactions on Communications, 57(11), 4372–4380.

    Google Scholar 

  17. Rezki, Z., & Alouini, M. S. (2012). Ergodic capacity of cognitive radio under imperfect channel state information. IEEE Transactions on Vehicular Technology, 61(5), 2108–2119.

    Article  Google Scholar 

  18. 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 

  19. Pandit, S., & Singh, G. (2015). Channel capacity in fading environment with CSI and interference power constraints for cognitive radio communication system. Wireless Networks, 21(4), 1275–1288.

    Article  Google Scholar 

  20. Asghari, V., & Aissa, S. (2010). Adaptive rate and power transmission in spectrum-sharing systems. IEEE Transactions on Wireless Communications, 9(10), 3272–3280.

    Article  Google Scholar 

  21. Abramowitz, M., & Stegun, I. A. (1972). Handbook of mathematical functions: With formulas, graphs, and mathematical tables (9th ed.). New York: Dover.

    MATH  Google Scholar 

  22. Asghari V. & Aissa S.(2008). Resource sharing in cognitive radio systems: outage capacity and power allocation under soft sensing. In Proceedings of the IEEE global telecommunications conference, (GLOBECOM’08), New Orleans, LA, USA, pp. 1–5.

  23. Kang, X., Liang, Y. C., Nallanathan, A., Garg, H. K., & Jhang, 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 

  24. Ghasemi, A., & Sousa, E. S (2006). Capacity of fading channels under spectrum-sharing constraints. In Proceedings of the IEEE international conference on communications (ICC’06), Istanbul, Turkey, pp. 4373–4378.

  25. Suzuki, H. (1977). A statistical model for urban multipath propagation. IEEE Transactions on Communication, 25(7), 673–680.

    Article  Google Scholar 

  26. Sheikh, A. U., Abdi, M., & Handforth, M. (1993). Indoor mobile radio channel at 946 MHz: Measurement and modelling, In Proceedings of IEEE vehicular technology (VTC’93), Secaucus, NJ, pp. 73–76.

  27. 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 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Indu Bala.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bala, I., Bhamrah, M.S. & Singh, G. Capacity in fading environment based on soft sensing information under spectrum sharing constraints. Wireless Netw 23, 519–531 (2017). https://doi.org/10.1007/s11276-015-1172-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-015-1172-0

Keywords

Navigation