Skip to main content
SpringerLink
Log in

Novel Markov channel predictors for interference alignment in cognitive radio network

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

In cognitive radio (CR) network, how to mitigate interference between different users is a key task. Interference alignment (IA) is a promising technique to tackle the multi-user interference in communication system. Compared with other interference management methods (such as zero-forcing), IA can not only effectively eliminate the interference, but also greatly increase the system capacity. However, the perfect channel state information (CSI) is required for both transmitters and receivers to apply the IA algorithm, which is hard to achieve in practical applications. In this paper, the effect of imperfect CSI on IA in CR system is analyzed in terms of signal to interference plus noise ratio and achievable sum rate. A linear finite state Markov chain (LFSMC) predictor, which incorporates the finite state Markov chain into the AR predictor, is proposed to reduce the impact of imperfect CSI on system performance of CR network. Moreover, for the sake of simplifying the initialization of LFSMC predictor, a simplified LFSMC (S-LFSMC) predictor is provided. Simulation results indicate that both of the LFSMC and S-LFSMC predictor can greatly improve the performance of IA system with the inaccurate CSI. Specifically, the LSFMC predictor can achieve satisfied performance compared with other predictors mentioned in this paper. And the LSFMC predictor which is simpler and its performance is still much better than traditional predictors. Therefore, we can choose a suitable predictor (LAFMC or S-LSFMC) based on the different requirements.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

References

  1. Wang, B., & Liu, K. (2011). Advances in cognitive radio networks: A survey. IEEE Journal of Selected Topics in Signal Processing, 5(1), 5–23.

    Article  Google Scholar 

  2. MacKenzie, A. B., Reed, J. H., Athanas, P., Bostian, C. W., Buehrer, R. M., DaSilva, L. A., et al. (2009). Cognitive radio and networking research at virginia tech. Proceedings of the IEEE, 97(4), 660–688.

    Article  Google Scholar 

  3. Joshi, G. P., Nam, S. Y., & Kim, S. W. (2013). Cognitive radio wireless sensor networks: Applications, challenges and research trends. Sensors, 13(9), 11196–11228.

    Article  Google Scholar 

  4. Farsani, R. K. (2015). On the capacity region of the broadcast, the interference, and the cognitive radio channels. IEEE Transactions on Information Theory, 61(5), 2600–2623.

    Article  MathSciNet  MATH  Google Scholar 

  5. Mirdamadi, A., & Sabbaghian, M. (2015). Spectrum sensing of interleaved SC-FDMA signals in cognitive radio networks. IEEE Transactions on Vehicular Technoloy, 64(4), 1633–1637.

    Article  Google Scholar 

  6. Shi, Z., McLernon, D., Ghogho, M., & Wu, Z. (2014). Improved spectrum sensing for OFDM cognitive radio in the presence of timing offset. EURASIP Journal on Wireless Communications and Networking, 2014, 224. doi:10.1186/1687-1499-2014-224.

    Article  Google Scholar 

  7. Mariani, A., Kandeepan, S., & Giorgetti, A. (2015). Periodic spectrum sensing with non-continuous primary user transmissions. IEEE Transactions on Wireless Communications, 14(3), 1636–1649.

    Article  Google Scholar 

  8. Huang, H., Li, Z., Si, J., & Guan, L. (2015). Underlay cognitive relay networks with imperfect channel state information and multiple primary receivers. IET Communications, 9(4), 460–467.

    Article  Google Scholar 

  9. Chen, Z., Wang, C. X., Hong, X., Thompson, J. S., Vorobyov, S. A., Ge, X., et al. (2012). Aggregate interference modeling in cognitive radio networks with power and contention control. IEEE Transactions on Communications, 60(2), 456–468.

    Article  Google Scholar 

  10. Jafar, S. A. & Shamai, S. S. (2008). Degrees of freedom region of the MIMO X channel. IEEE Transactions on Information Theory, 54(1), 151–170. IEEE global telecommunications conference (GLOBECOM 07), Washington, DC, Nov 26–30, 2007.

  11. Cadambe, V., & Jafar, S. (2008). Interference alignment and spatial degrees of freedom for the k-user interference channel. In Communications, 2008. ICC’08. IEEE international conference on, May 2008 (pp. 971–975).

  12. Razaviyayn, M., Lyubeznik, G., & Luo, Z.-Q. (2012). On the degrees of freedom achievable through interference alignment in a MIMO interference channel. IEEE Transactions on Signal Processing, 60(2), 812–821.

    Article  MathSciNet  Google Scholar 

  13. Ntranos, V., Maddah-Ali, M. A., & Caire, G. (2015). Cellular interference alignment. IEEE Transactions on Information Theory, 61(3), 1194–1217.

    Article  MathSciNet  MATH  Google Scholar 

  14. Krishnamurthy, S. R., Ramakrishnan, A., & Jafar, S. A. (2015). Degrees of freedom of rank-deficient MIMO interference channels. IEEE Transactions on Information Theory, 61(1), 341–365.

    Article  MathSciNet  MATH  Google Scholar 

  15. Chaaban, A., & Sezgin, A. (2016). The approximate capacity region of the symmetric k-user Gaussian interference channel with strong interference. IEEE Transactions on Information Theory, 62(99), 1.

  16. Yi, X., & Gesbert, D. (2013). Precoding methods for the MISO broadcast channel with delayed CSIT. IEEE Transactions on Wireless Communications, 12(5), 2344–2354.

    Article  Google Scholar 

  17. Shrestha, R., Bae, I., & Kim, J. M. (2014). A leakage-based solution for interference alignment in MIMO interference channel networks. KSII Transactions on Internet and Information Systems, 8(2), 424–442.

    Article  Google Scholar 

  18. Sharma, S., Chatzinotas, S., & Ottersten, B. (2013). Interference alignment for spectral coexistence of heterogeneous networks. EURASIP Journal on Wireless Communications and Networking, 2013(1), 46.

    Article  Google Scholar 

  19. Castanheira, D., Silva, A., & Gameiro, A. (2015). Limited inter-system information exchange method for heterogeneous networks. IEEE Communications Letters, 19(9), 1656–1659.

    Article  Google Scholar 

  20. Castanheira, D., Silva, A., Dinis, R., & Gameiro, A. (2015). efficient transmitter and receiver designs for SC-FDMA based heterogeneous networks. IEEE Transactions on Communications, 63(7), 2500–2510.

    Article  Google Scholar 

  21. Li, X., Zhao, N., Sun, Y., & Yu, F. R. (2016). Interference alignment based on antenna selection with imperfect channel state information in cognitive radio networks. IEEE Transactions on Vehicular Technology, 65(7), 5497–5511.

    Article  Google Scholar 

  22. Castanheira, D., Silva, A., & Gameiro, A. (2014). Set optimization for efficient interference alignment in heterogeneous networks. IEEE Transactions on Wireless Communications, 13(10), 5648–5660.

    Article  Google Scholar 

  23. Xie, B., Li, Y., Minn, H., & Nosratinia, A. (2013). Adaptive interference alignment with CSI uncertainty. IEEE Transactions on Communications, 61(2), 792–801.

    Article  Google Scholar 

  24. Abdoli, M. J., Ghasemi, A., & Khandani, A. K. (2013). On the degrees of freedom of K-user SISO interference and X channels with delayed CSIT. IEEE Transactions on Information Theory, 59(10), 6542–6561.

    Article  MathSciNet  MATH  Google Scholar 

  25. Chen, X., & Yuen, C. (2016). On interference alignment with imperfect CSI: Characterizations of outage probability, ergodic rate and ser. IEEE Transactions on Vehicular Technology, 65(1), 47–58.

    Article  Google Scholar 

  26. Dong, A., Zhang, H., & Yuan, D. (2013). Achievable rate improvement through channel prediction for interference alignment. In Communications (APCC), 2013 19th Asia-Pacific conference on, August 2013 (pp. 293–298).

  27. Shi, Z., Wu, Z., Yin, Z., & Zhuang, S. (2014). A novel channel predictor for interference alignment in cognitive radio network. In Wireless personal multimedia communications (WPMC), 2014 international symposium on, September 2014 (pp. 396–401).

Download references

Acknowledgements

The research in this article is supported by ‘the National Natural Science Foundation of China’ (Grant Nos. 61102084 and 61471142).

Author contributions

ZS proposed the algorithms, wrote the paper; ZW gave instructions of the research, and revised the paper; ZY, ZY and QC improved the algorithms and revised the manuscript.

Author information

Authors and Affiliations

  1. School of Electronics and Information Engineering, Harbin Institute of Technology, 92 Xidazhi Street, Harbin, China

    Zhenguo Shi, Zhilu Wu, Zhendong Yin & Zhutian Yang

  2. Department of Computing and Communications, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia

    Qingqing Cheng

Authors
  1. Zhenguo Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhilu Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhendong Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhutian Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Qingqing Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhilu Wu.

Ethics declarations

Conflicts of interest

The authors declare no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shi, Z., Wu, Z., Yin, Z. et al. Novel Markov channel predictors for interference alignment in cognitive radio network. Wireless Netw 24, 1915–1925 (2018). https://doi.org/10.1007/s11276-017-1445-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-017-1445-x

Keywords

Search

Navigation