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A Spectral Distance Based Power Control Scheme for Capacity Enhancement of OFDM Cognitive Radio

Abstract

This work investigates power allocation algorithms for capacity maximization of a non-contiguous orthogonal frequency division multiplexing (NC-OFDM) based cognitive radio (CR) user coexisting with multiple active primary users (PU), under total power budget, sub-channel power and overall interference constraints. Based on the spectral distance between specific subcarriers and PU bands, the scheme allocates power among a minimum number of PU adjacent subcarriers with a water level that is different from the ‘far-away’ subcarriers. The proposed ‘n-adjacent’ approach is subjected to both aggregate and individual PU band interference constraint. Unlike some of the existing methods, the proposed method is capable of meeting the constraints including the contribution of ‘good quality’ subcarriers even when they are close to PU bands. Comparison of results for a wide range of power budget shows that the improved method, using the same complexity, can achieve higher cognitive user capacity with greater power allocation efficiency.

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References

  1. 1.

    Mitola, J. (1999). Cognitive radio for flexible mobile multimedia communications. IEEE international workshop on mobile multimedia communication (pp. 3–10). McLean, VA, USA.

  2. 2.

    Federal Communication Commission. Spectrum policy task force report FCC Report 2002, ET Docket, 02–135.

  3. 3.

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

    Article  Google Scholar 

  4. 4.

    Hossain, E., Niyato, D., & Kim, D. I. (2013). Evolution and future trends of research in cognitive radio: a contemporary survey. Wireless Communications and Mobile Computing. doi:10.1002/wcm.2443.

  5. 5.

    Mahmoud, H. A., Yücek, T., & Arslan, H. (2009). OFDM for cognitive radio: Merits and challenges. IEEE Wireless Communication, 16(2), 6–15.

    Article  Google Scholar 

  6. 6.

    Weiss, T., Hillenbrand, J., AKrohn, A., & Jondral, F. K. (2004). Mutual interference in OFDM-based spectrum pooling systems. IEEE VTC, 4, 1873–1877.

    Google Scholar 

  7. 7.

    Zhang, H., Ruyet, D. L., & Terre, M. (2009). Spectral efficiency comparison between OFDM/OQAM- and OFDM-based CR networks. Wireless Communications and Mobile Computing, 9(11), 1487–1501.

    Article  Google Scholar 

  8. 8.

    Pao, W. C., & Chen, Y. F. (2014). Adaptive gradient-based methods for adaptive power allocation in OFDM-based cognitive radio networks. IEEE Transactions on Vehicular Technology, 63(2), 836–848.

    Article  Google Scholar 

  9. 9.

    Cover, T. M., & Thomas, J. A. (2006). Elements of information theory. New Jersey: Wiley.

    MATH  Google Scholar 

  10. 10.

    Bansal, G., Hossain, M. J., & Bhargava, V. K. (2011). Adaptive power loading for OFDM-based cognitive radio systems with statistical interference constraint. IEEE Transactions on Letters, 10(9), 2786–2791.

    Google Scholar 

  11. 11.

    Zhang, Q., Feng, Z., Yang, T., & Li, W. (2015). Optimal power allocation and relay selection in multi-hop cognitive relay networks. Wireless Personal Communication, 86, 1673–1692.

    Article  Google Scholar 

  12. 12.

    Benaya, A. M., Shokair, M., El-Rabaie, E. S., & Elkordy, M. F. (2015). Optimal power allocation for sensing-based spectrum sharing in MIMO cognitive relay networks. Wireless Personal Communication, 82, 2695–2707.

    Article  Google Scholar 

  13. 13.

    Rasouli, H., Kong, H. Y., & Anpalagan, A. (2014). Cooperative subcarrier allocation and power allocation in the downlink of an amplify-and-forward OFDM relaying system. Wireless Personal Communication, 79, 2271–2290.

    Article  Google Scholar 

  14. 14.

    Zhang, Y., & Leung, C. (2009). Resource allocation in an OFDM-based cognitive radio system. IEEE Transactions on Communications, 57(7), 1928–1931.

    Article  Google Scholar 

  15. 15.

    Zhang, T., Chen, W., Han, Z., & Cao, Z. (2014). Hierarchic power allocation for spectrum sharing in OFDM-based cognitive radio networks. IEEE Transactions on Vehicular Technology, 63(8), 4077–4091.

    Article  Google Scholar 

  16. 16.

    Biyanwilage, S., Gunawardana, U., & Liyanapathirana, R. (2014). Power allocation in OFDM cognitive radio relay networks with outdated channel state information. International Journal of Communication Systems. doi:10.1002/dac.2755.

    Google Scholar 

  17. 17.

    Peng, J., Li, S., Zhu, C., Liu, W., Zhu, Z., & Lin, K. C. (2015). A joint subcarrier selection and power allocation scheme using variational inequality in OFDM-based cognitive relay networks. Wireless Communications and Mobile Computing. doi:10.1002/wcm.2581.

    Google Scholar 

  18. 18.

    Shaat, M., & Bader, F. (2010). Computationally efficient power allocation algorithm in multicarrier-based cognitive radio networks: OFDM and FBMC systems. EURASIP Journal on Advances in Signal Processing. doi:10.1155/2010/528378.

    Google Scholar 

  19. 19.

    Gupta, J., Karwal, V., & Dwivedi, V. K. (2015). Joint overlay-underlay optimal power allocation in cognitive radio. Wireless Personal Communication, 83, 2267–2278.

    Article  Google Scholar 

  20. 20.

    Chen, Y., Lei, Q., & Yuan, X. (2014). Resource allocation based on dynamic hybrid overlay/underlay for heterogeneous services of cognitive radio networks. Wireless Personal Communication, 79, 1647–1664.

    Article  Google Scholar 

  21. 21.

    Mao, J., Xie, G., Gao, J., & Liu, Y. (2013). Energy efficiency optimization for OFDM-based cognitive radio systems: A water-filling factor aided search method. IEEE Transactions on Wireless Communications, 12(5), 2366–2375.

    Article  Google Scholar 

  22. 22.

    Vinh, N. V., Shouyi, Y., & Tran, L. C. (2014). Power allocation algorithm in OFDM-based cognitive radio systems (pp. 13–18). Da Nang: GLOBECOM.

    Google Scholar 

  23. 23.

    Kang, X., Garg, H. K., Liang, Y. C., & Zhang, R. (2010). Optimal power allocation for OFDM-based cognitive radio with new primary transmission protection criteria. IEEE Transactions on Wireless Communications, 9(6), 2066–2075.

    Article  Google Scholar 

  24. 24.

    Sahin, A., & Arslan, H. (2011). Edge windowing for OFDM based systems. IEEE Communications Letters, 15(11), 1208–1211.

    Article  Google Scholar 

  25. 25.

    Wang, P., Zhao, M., Xiao, L., Zhou, S., & Wang, J. (2007). Power allocation in OFDM-based cognitive radio system. In Global Telecommunications Conference, 2007. GLOBECOM '07. IEEE (pp. 4061–4065). Washington, DC: IEEE.

  26. 26.

    Hasan, Z., Bansal, G., Hossain, E., & Bhargava, V. K. (2009). Energy-efficient power allocation in OFDM-based cognitive radio systems: A risk-return model. IEEE Transactions on Wireless Communications, 8(12), 6078–6088.

    Article  Google Scholar 

  27. 27.

    Rajbanshi, R., Wyglinski, A. M., & Minden, G. J. (2007). Cognitive radio communication networks, Ch. 5. New York: Springer.

    Google Scholar 

  28. 28.

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

    Book  MATH  Google Scholar 

  29. 29.

    Bepari, D., & Mitra, D. (2015). Improved power loading scheme for orthogonal frequency division multiplexing based cognitive radio. IET Communications, 9(16), 2033–2040.

    Article  Google Scholar 

  30. 30.

    Bansal, G., Hossain, M. J., & Bhargava, V. K. (2008). Optimal and suboptimal power allocation schemes for ofdm-based cognitive radio systems. IEEE Transactions on Wireless Communications, 7(11), 4710–4718.

    Article  Google Scholar 

Download references

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Correspondence to Dipen Bepari.

Appendix: Proof of Theorem 1

Appendix: Proof of Theorem 1

We use the fact that maximizing a concave function (3) is nothing but a minimizing of its negative value. Considering the Lagrange parameters λ, β j , δ and μ i for (i), (ii), (iii) and (iv) inequality constraints of (3) respectively. The KKT conditions are written as [28]

$$\lambda \ge 0$$
(10)
$$\beta_{j} \ge 0,\quad \forall j \in \left\{ {1,2, \ldots ,L} \right\}$$
(11)
$$\delta \ge 0$$
(12)
$$\mu_{i} \ge 0,\quad \forall i \in \left\{ {1,2, \ldots ,N} \right\}$$
(13)
$$\lambda \left( { \, \sum\limits_{i = 1}^{N} {P_{i} } - P_{T} } \right) = 0$$
(14)
$$\beta_{j} \left( { \, P_{j}^{T} - P_{j}^{sc} } \right) = 0,\quad \forall j \in \left\{ {1,2, \ldots ,L} \right\}$$
(15)
$$\delta \left( { \, \sum\limits_{i = 1}^{N} {\alpha_{i} P_{i} - I_{th} } } \right) = 0$$
(16)

and

$$\mu_{i} P_{i} = 0,\quad \forall i \in \left\{ {1,2, \ldots ,N} \right\}$$
(17)
$$- \frac{1}{{h_{i}^{ - 1} + P_{i} }} - \mu_{i} + \lambda + \beta_{j,\phi \left( i \right)} + \delta \alpha_{i}= 0,\quad \forall i \in \left\{ {1,2, \ldots ,N} \right\}$$
(18)

where \(h_{i} = \frac{{\left| {G_{i}^{ss} } \right|^{2} }}{{\sigma_{2} + \gamma_{i} }}\). From (18) it can be written that

$$\mu_{i} = \lambda + \beta_{j,\phi \left( i \right)} + \delta \alpha_{i}- \frac{1}{{h_{i}^{ - 1} + P_{i} }}$$
(19)

Now, substituting (19) into (17)

$$P_{i} \left( {\lambda + \beta_{j,\phi \left( i \right)} + \delta \alpha_{i}- \frac{1}{{h_{i}^{ - 1} + P_{i} }}} \right) = 0,\quad \forall i \in \left\{ {1,2, \ldots ,N} \right\}$$
(20)

and substituting (19) into (13)

$$P_{i} \ge \frac{1}{{\lambda + \beta_{j,\phi \left( i \right)} + \delta \alpha_{i}}} - h_{i}^{ - 1} ,\quad \forall i$$
(21)

From (21), P i  > 0 if λ + β j,ϕ(i) + δα i  < h −1 i and from (20) P i expressed as \(P_{i} = \frac{1}{{\lambda + \beta_{j,\phi \left( i \right)} + \delta \alpha_{i}}} - h_{i}^{ - 1}\). On the other hand, if λ + β j,ϕ(i) + δα i  ≥ h −1 i then due to violation of (20), P i  > 0 is impossible and the only solution is P i  = 0. Combining these two results, the solution is expressed as

$$P_{i} = \left[ {\frac{1}{{\lambda + \beta_{j,\phi \left( i \right)} + \delta \alpha_{i}}} - \frac{{\sigma_{2} + \gamma_{i} }}{{\left| {G_{i}^{ss} } \right|^{2} }}} \right]^{ + } ,\quad \forall i$$
(22)

The theorem is proved.

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Bepari, D., Bojja, A.K., Kumar, B.S. et al. A Spectral Distance Based Power Control Scheme for Capacity Enhancement of OFDM Cognitive Radio. Wireless Pers Commun 90, 157–173 (2016). https://doi.org/10.1007/s11277-016-3337-2

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Keywords

  • Cognitive radio
  • OFDM
  • Subcarrier power allocation
  • Interference threshold