Green cognitive communication approaches

  • Abstract
  • Keywords
  • References
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  • Abstract

    Generally one of the reasons for global warming is Co2 emission. The rapid growth of wireless communication is the reason for more emission. This creates more health issues, especially children. To reduce the issues, now the wireless communication is tilted towards green communication. Green communication cannot be achieved in single step. In this paper some of techniques are followed to achieve sustainable communication. The main objective of this paper is cognitive radio (CR), which makes use of spectrum in efficient way. In our proposed system, we focused on green cognitive cycle, Cognitive radio-Ultra wide band (CR-UWB) and Cognitive radio-Orthogonal Frequency Division Multiplexing (CR-OFDM). With these above methods we achieved better achievements in terms of efficiency, less time delay, resource utilization and low power for 5G wireless green communication.


  • Keywords

    Cognitive Radio; Sharing; UWB; OFDM; Power Spectral Density (PSD)

  • References

      [1] Mitola J, M. G. “Cognitive radio: Making software radios more personal”, IEEE Personal Communications, 1999.

      [2] S. Haykin, “Cognitive radio: Brain-empowered wireless communications”, IEEE J. Sel. Areas Commun., vol. 23, no. 2, pp. 201–220, Feb.2005.

      [3] A. Sharma and C. R.Murthy, “A group testing based spectrum hole search using a simple sub-Nyquist sampling scheme,” in Proc. GLOBECOM, pp. 1526–1531, 2012.

      [4] Y. C. Liang, K. C. Chen, G. Y. Li, and P. Mahonen, “Cognitive radio networking and communications: An overview”, IEEE Trans. Veh. Technol., vol. 60, no. 7, pp. 3386–3407, Sep. 2011.

      [5] T. Yucek and H. Arslan, “A survey of spectrum sensing algorithms for cognitive radio applications”,IEEE Commun. Surv.Tuts., vol. 11, no. 1, pp. 116–130, First Quart., 2009.

      [6] Y. Zeng, Y. C. Liang, A. T. Hoang, and R. Zhang, “A review on spectrum sensing for cognitive radio: Challenges and solutions”, EURASIP J. Adv.Signal Process., vol. 2010, pp. 381 465-1–381 465-15, Jan. 2010.

      [7] E. Axell, G. Leus, E. G. Larsson, and H. V. Poor, “Spectrum sensing for cognitive radio: State-of-the-art and recent advances”, IEEE SignalProcess. Mag., vol. 29, no. 3, pp. 101–116, May 2012.

      [8] Trigui, E.; Esseghir, M.; Boulahia, L.M., "Spectrum handoff algorithm for mobile cognitive radio users based on agents' negotiation", Wireless and Mobile Computing, Networking and Communications (WiMob), 2013 IEEE 9th International Conference, vol., no., pp.750, 756, 7-9 Oct. 2013.

      [9] Lu Li; YanmingShen; Keqiu Li; Kai Lin, "TPSH: “A Novel Spectrum Handoff Approach Based on Time Estimation in Dynamic spectrum networks”," Computational Science and Engineering (CSE), 2011 IEEE 14th International Conference, vol., no., pp.345,350, 24-26 Aug. 2011.

      [10] Christian, I; Moh, S.; Ilyong Chung; Jinyi Lee, "Spectrum mobility in cognitive radio networks”,Communications Magazine, IEEE, vol.50, no.6, pp.114,121, June 2012 doi:10.1109/MCOM.2012.6211495.

      [11] M Matthe, LL Mendes, I Gaspar, N Michailow, D Zhang, G Fettweis, “Multi-user time-reversal STC-GFDMA for future wireless networks”,EURASIP J. Wirel. Commun. Netw, 2015, 132 (2015).

      [12] M Taherzadeh, H Nikopour, ABayesteh, H Baligh, in Proc. IEEE VTC-Fall. CMA codebook design, (2014).

      [13] Mohandass Sundararajan and Umamaheswari Govindaswamy, “Multicarrier Spread Spectrum Modulation Schemes andEfficient FFT Algorithms for Cognitive Radio Systems” Electronics 2014, 3, 419-443; doi: 10.3390/electronics3030419 (2014).




Article ID: 12523
DOI: 10.14419/ijet.v7i2.26.12523

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