Skip to main content

Advertisement

Springer Nature Link
Log in

Optimal Resource Allocation in Heterogeneous MIMO Cognitive Radio Networks

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

The optimal resource allocation in MIMO cognitive radio networks with heterogeneous secondary users, centralized and distributed users, is investigated in this work. The core aim of this work is to study the joint problems of transmission time and power allocation in a MIMO cognitive radio scenario. The optimization objective is to maximize the total capacity of the secondary users (SUs) with the constraint of fairness. At first, the joint problems of transmission time and power allocation for centralized SUs in uplink is optimized. Afterwards, for the heterogeneous case with both the centralized and distributed secondary users, the resource allocation problem is formulated and an iterative power water-filling scheme is proposed to achieve the optimal resource allocation for both kinds of SUs. A dynamic optimal joint transmission time and power allocation scheme for heterogeneous cognitive radio networks is proposed. The simulation results illustrate the performance of the proposed scheme and its superiority over other power control schemes.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

References

  1. FCC. (2010). Spectrum policy task force report. ET Docket No. 02–380 and No. 04–186, Sep. 2010.

  2. Adian, M. G., & Aghaeinia, H. (2012). Spectrum sharing and power allocation in multiple-in multiple-out cognitive radio networks via pricing. IET Communications, 6(16), 2621–2629.

    Article  MATH  MathSciNet  Google Scholar 

  3. Seung-Jun, Kim., & Giannakis, G. B. (2011). Optimal resource allocation for MIMO ad hoc cognitive radio networks. IEEE Transactions on Information Theory, 57(5), 3117–3131.

  4. Zhang, R., & Liang, Y. C. (2008). Exploiting multi-antennas for opportunistic spectrum sharing in cognitive radio networks. IEEE Journal of Selected Topics in Signal Processing, 2(1), 88–102.

    Article  Google Scholar 

  5. Zhang, L., Liang, Y. C., & Xin, Y. (2008). Joint beamforming and power allocation for multiple access channels in cognitive radio networks. IEEE Journal on Selected Areas in Communications, 26(1), 38–51.

    Article  MATH  Google Scholar 

  6. Scutari, G., & Palomar, D. P. (2010). MIMO cognitive radio: A game theoretical approach. IEEE Transactions on Signal Processing, 58(2), 761–779.

    Article  MathSciNet  Google Scholar 

  7. Xie, R., Yu, F., Ji, H., & Li, Y. (2012). Energy-efficient resource allocation for heterogeneous cognitive radio networks with femtocells. IEEE Transactions on Wireless Communications, 11(11), 3910–3920.

    Article  Google Scholar 

  8. Su, W., Matyjas, J. D., & Batalama, S. N. (2012). Active cooperation between primary users and cognitive radio users in heterogeneous Ad Hoc networks. IEEE Transactions on Signal Processing, 60(4), 1796–1805.

    Article  MathSciNet  Google Scholar 

  9. Ge, M., & Wang, S. (2012). Fast optimal resource allocation is possible for multiuser OFDM-based cognitive radio networks with heterogeneous services. IEEE Transactions on Wireless Communications, 11(4), 1500–1509.

    Article  Google Scholar 

  10. Wang, S., Zhou, Z., Ge, M., & Wang, C. (2013). Resource allocation for heterogeneous cognitive radio networks with imperfect spectrum sensing. IEEE Journal on Selected Areas in Communications, 31(3), 464–475.

    Article  Google Scholar 

  11. Ni, Q., & Zarakovitis, C. (2012). Nash bargaining game theoretic scheduling for joint channel and power allocation in cognitive radio systems. IEEE Journal on Selected Areas in Communications, 30(1), 70–81.

    Article  Google Scholar 

  12. Chen, J., & Swindlehurst, A. (2012). Applying bargaining solutions to resource allocation in multiuser MIMO-OFDMA broadcast systems. IEEE Journal of Selected Topics in Signal Processing, 6(2), 127–139.

    Article  Google Scholar 

  13. Xu, H., & Li, B. (2010). Efficient resource allocation with flexible channel cooperation in OFDMA cognitive radio networks In Proceedings of IEEE INFOCOM conference, pp. 561–569.

  14. Boyd, S., & Vandenberghe, L. (2004). Convex optimization. Cambridge: Cambridge University Press.

    Book  MATH  Google Scholar 

  15. Magnus, J. R., & Neudecker, H. (1999). Matrix differential calculus with applications in statistics and economics (2nd ed.). Wiley: New York.

    Google Scholar 

  16. Osborne, M. J., & Rubenstein, A. (1994). A course in game theory. Cambridge: MIT Press.

    MATH  Google Scholar 

  17. Yu, W., Ginis, G., & Cioffi, J. M. (2002). Distributed multiuser power control for digital subscriber lines. IEEE Journal on Selected Areas in Communications, 20(5), 1105–1115.

    Article  Google Scholar 

  18. Boyd, S., & Mutapcic, A. (2007). Subgradient methods, notes for EE364. Standford: Standford University.

    Google Scholar 

  19. Shum, K. W., Leung, K., & Sung, C. (2007). Convergence of iterative waterfilling algorithm for gaussian interference channels. IEEE Journal on Selected Areas in Communications, 25(2), 1091–1100.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Electrical Engineering Department, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran

    Mehdi Ghamari Adian & Hassan Aghaeinia

Authors
  1. Mehdi Ghamari Adian
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Hassan Aghaeinia
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Mehdi Ghamari Adian.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Adian, M.G., Aghaeinia, H. Optimal Resource Allocation in Heterogeneous MIMO Cognitive Radio Networks. Wireless Pers Commun 76, 23–39 (2014). https://doi.org/10.1007/s11277-013-1486-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-013-1486-0

Keywords