Advertisement

Spectrum Prediction in Cognitive Radio Based on Sequence to Sequence Neural Network

  • Ling XingEmail author
  • Mingbing Li
  • Yihe Wan
  • Qun Wan
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 302)

Abstract

Cognitive radio provides the ability to access the spectrum that is not used by primary users in an opportunistic manner, enabling dynamic spectrum access technology and improving spectrum utilization. The spectrum prediction plays an important role in key technologies such as spectrum sensing, spectrum decision, spectrum sharing and spectrum mobility in cognitive radio. In this paper, aiming at the spectrum prediction problem in cognitive radio, a spectrum prediction technique based on the sequence to sequence (seq-to-seq) network model constructed by the GRU basic network module is proposed. Due to the long and short time memory function of the GRU network structure, its performance is better than the previous Multi-Layer Perception (MLP) network model. This paper also explores in depth the impact of changes in the length of the input sequence on the prediction results. And the proposed seq-to-seq network model also performs well for multi-slot prediction and multi-channel joint prediction.

Keywords

Cognitive radio Spectrum prediction Sequence to sequence network model 

References

  1. 1.
    Mitola, J., Maguire, J.: Cognitive radio: making software radios more personal. IEEE Pers. Commun. 6(4), 13–18 (1999)CrossRefGoogle Scholar
  2. 2.
    Yucek, T., Arslan, H.: A survey of spectrum sensing algorithms for cognitive radio applications. Commun. Surv. Tutor. 11(1), 116–130 (2009)CrossRefGoogle Scholar
  3. 3.
    Wang, S., Wang, Y., Coon, J.: Energy-efficient spectrum sensing and access for cognitive radio networks. IEEE Trans. Veh. Technol. 61(2), 906–912 (2012)CrossRefGoogle Scholar
  4. 4.
    Xing, X., Jing, T., Cheng, W.: Spectrum prediction in cognitive radio networks. IEEE Wirel. Commun. 20(2), 90–96 (2013)CrossRefGoogle Scholar
  5. 5.
    Chen, Z., Guo, N., Hu, Z.: Channel state prediction in cognitive radio, part ii: single-user prediction. In: 2011 Proceedings of IEEE SoutheastCon, Nashville, pp. 50–54. IEEE (2011)Google Scholar
  6. 6.
    Xing, X.: Channel quality prediction based on Bayesian inference in cognitive radio networks. In: 2013 Proceedings IEEE INFOCOM, Turin, pp. 1465–1473. IEEE (2013)Google Scholar
  7. 7.
    Tumuluru, V.-K., Wang, P., Niyato, D.: Channel status prediction for cognitive radio networks. Wirel. Commun. Mob. Comput. 12(10), 862–874 (2012)CrossRefGoogle Scholar
  8. 8.
    Tumuluru, V.-K., Wang, P., Niyato, D.: A neural network based spectrum prediction scheme for cognitive radio. In: 2010 IEEE International Conference on Communications, Cape Town, pp. 1–5. IEEE (2010)Google Scholar
  9. 9.
    Liang, Y., Yin, S., Hong, W.: Spectrum behavior learning in cognitive radio based on artificial neural network. In: MILCOM 2011 Military Communications Conference, Baltimore. IEEE (2012)Google Scholar
  10. 10.
    Zhao, J.-L., Wang, M., Yuan, J.: Based on neural network spectrum prediction of cognitive radio. In: International Conference on Electronics. IEEE (2011)Google Scholar
  11. 11.
    Nakisa, S., Mousavinia, A., Amirpour, H.: A channel state prediction for multi-secondary users in a cognitive radio based on neural network. In: International Conference on Electronics. IEEE (2013)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  1. 1.School of Information and Communication EngineeringUniversity of Electronic Science and Technology of ChinaChengduChina
  2. 2.Southwest Institute of Electronic TechnologyChengduChina
  3. 3.Jiangxi Province Engineering Research Center of Spacial Wireless CommunicationsNanchangChina
  4. 4.School of Information and Communication EngineeringUniversity of Electronic Science and Technology of ChinaChengduChina

Personalised recommendations