Cognitive radio networks for green wireless communications: an overview

Abstract

As power consumption results in greenhouse gas emissions and energy costs for operators, analyzing power consumption in wireless networks and portable devices is of crutial importance. Due to environmental effects resulted from energy generation and exploitation as well as the cost of surging energy, energy-aware wireless systems attract unprecedented attention. Cognitive Radio (CR) is one of the optimal solutions that allows for energy savings on both the networks and devices. Thus, cognitive radio contributes to increase spectral and energy efficiency as well as reduction in power consumption. In addition, energy consumption of the CR technologies as intelligent technology should be considered to realize the green networks objective. In this article, we look into energy efficiency of the cognitive wireless network paradigms. Moreover, energy efficiency analysis and modelling in these systems are specifically focused on achieving green communications objectives. However, CRs by altering all elements of wireless data communications are considered in this paper, and the energy-efficient operation and energy efficiency enabler perspectives of CRs are also analyzed.

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Notes

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    For a wireless system, EE also relies on distance, carrier frequency, the efficiency of antennas, and etc., according to the radio environment.

References

  1. 1.

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

    Article  Google Scholar 

  2. 2.

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

    Article  Google Scholar 

  3. 3.

    Huang, X., Han, T., & Ansari, N. (2015). On green-energy-powered cognitive radio networks. IEEE Communications Surveys & Tutorials, 17(2), 827–842.

    Article  Google Scholar 

  4. 4.

    Han, C., Harrold, T., Armour, S., et al. (2011). Green radio: Radio techniques to enable energy-efficient wireless networks. IEEE Communications Magazine, 49(6), 46–54.

    Article  Google Scholar 

  5. 5.

    Correia, L. M., Zeller, D., Blume, O., et al. (2010). Challenges and enabling technologies for energy aware mobile radio networks. IEEE Communications Magazine, 48(11), 66–72.

    Article  Google Scholar 

  6. 6.

    Hasan, Z., Boostanimehr, H., & Bhargava, V. K. (2011). Green cellular networks: A survey, some research issues and challenges. IEEE Communications Surveys & Tutorials, 13(4), 524–540.

    Article  Google Scholar 

  7. 7.

    Conte, A., Feki, A., Chiaraviglio, L., et al. (2011). Cell wilting and blossoming for energy efficiency. IEEE Wireless Communications, 18(5), 50–57.

    Article  Google Scholar 

  8. 8.

    Niu, Z. (2011). TANGO: Traffic-aware network planning and green operation. IEEE Wireless Communications, 18(5), 25–29.

    Article  Google Scholar 

  9. 9.

    Gong, J., Yang, Z., Niu, Z., & Wu, Y. (2010). Cell zooming for cost-efficient green cellular networks. IEEE Communications Magazine, 48(11), 74–79.

    Article  Google Scholar 

  10. 10.

    Liang, X., Lu, R., Li, X., & Shen, X. (2011). GRS: The green, reliability, and security of emerging machine to machine communications. IEEE Communications Magazine, 49(4), 28–35.

    Article  Google Scholar 

  11. 11.

    Kim, S. (2017). Fog radio access network system control scheme based on the embedded game model. EURASIP Journal on Wireless Communications and Networking, 2017(113), 1–14.

    Google Scholar 

  12. 12.

    Peng, M., & Zhang, K. C. (2016). Recent advances in fog radio access networks: performance analysis and radio resource allocation. IEEE Access, 4, 5003–5009.

    Article  Google Scholar 

  13. 13.

    Chandrasekhar, V., Andrews, J. G., & Gatherer, A. (2008). Femtocell networks: A survey. IEEE Communications Magazine, 46(9), 59–67.

    Article  Google Scholar 

  14. 14.

    Yousafzai, A., Gani, A., et al. (2017). Cloud resource allocation schemes: Review, taxonomy, and opportunities. Knowledge and Information Systems, 50(2), 347–381.

    Article  Google Scholar 

  15. 15.

    Zappone, A., & Jorswieck, E. A. (2017). Energy-efficient resource allocation in future wireless networks by sequential fractional programming. Digital Signal Processing, 60, 324–337.

    Article  Google Scholar 

  16. 16.

    Mumford, R. (2016). 5G manifesto for deployment of 5G in Europe. Norwood: Horizon House Publications Inc.

    Google Scholar 

  17. 17.

    Dejonghe, A., Bougard, B., Pollin, S., et al. (2007). Green reconfigurable radio systems. IEEE Signal Processing Magazine, 24(3), 90–101.

    Article  Google Scholar 

  18. 18.

    Gür, G., & Alagöz, F. (2011). Green wireless communications via cognitive dimension: An overview. IEEE Network, 25(2), 50–56.

    Article  Google Scholar 

  19. 19.

    Han, T., & Ansari, N. (2014). Powering mobile networks with green energy. IEEE Wireless Communications, 21(1), 90–96.

    Article  Google Scholar 

  20. 20.

    Huang, X., Yu, R., Kang, J., et al. (2017). Software defined energy harvesting networking for 5G green communications. IEEE Wireless Communications, 24(4), 38–45.

    Article  Google Scholar 

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Correspondence to Hengameh Keshavarz.

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Ostovar, A., Keshavarz, H. & Quan, Z. Cognitive radio networks for green wireless communications: an overview. Telecommun Syst (2020). https://doi.org/10.1007/s11235-020-00703-8

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Keywords

  • Wireless communications
  • Green communications
  • Energy efficiency
  • Cognitive radio
  • Long-term evolution (LTE)