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Optimization of green RNP problem for LTE networks using possibility theory

  • Soufiane DahmaniEmail author
  • Mohammed Gabli
  • El Bekkaye Mermri
  • Abdelhafid Serghini
Original Article
  • 16 Downloads

Abstract

At present, the demand for natural energy has been ever increasing, so energy has become a major concern for everyone. As Long Term Evolution Base Stations consume a large amount of the total energy expenditure in a cellular network, it is of keen interest to researchers to reduce the energy consumed by BSs when considering network planning. In this paper, we consider the green radio network planning problem for the LTE cellular networks. Our aim is to reduce energy consumption by reducing the number of active BSs, which will also reduce the production of carbon dioxide. Now BSs are currently operated and deployed for the worst traffic peak estimates. However, traffic fluctuates with time depending on the mobile stations behavior and their data needs. From our point of view, in order to investigate more realistic cases, we consider the situation where the traffic information is taken as imprecise and uncertain value. So, we introduce a model of problem where each traffic is a fuzzy variable, and then, we present a decision-making model based on possibility theory. To solve the problem, we propose a solution method using genetic algorithms and a dynamic Evolved Node B switching on/off strategy. The obtained results showed the efficiency of our approach and demonstrated considerable energy saving, through dynamic adaptation of the number of active BSs.

Keywords

Green LTE network planning BS switching Energy consumption Genetic algorithms Possibility theory 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    Aydin ME, Kwan R, Wu J, Zhang J (2011) Multiuser scheduling on the LTE downlink with simulated annealing. In: 2011 IEEE 73rd vehicular technology conference (VTC Spring), pp 1–5Google Scholar
  2. 2.
    Gu J, Ruan Y, Chen X, Wang C (2011) A novel traffic capacity planning methodology for LTE radio network dimensioning. In: IET international conference on communication technology and application (ICCTA), pp 462–466Google Scholar
  3. 3.
    Lister D (2009) An operators view on green radio. In: IEEE international conference on communications (ICC) workshopsGoogle Scholar
  4. 4.
    Zhang S, Chau KW (2009) Dimension reduction using semi-supervised locally linear embedding for plant leaf classification. In: Huang DS et al (eds) Emerging intelligent computing technology and applications. ICIC 2009, vol 5754. Lecture notes in computer science, pp 948–955Google Scholar
  5. 5.
    Ardabili SF, Najafi B, Shamshirband S, Bidgoli BM, Deo RC, Chau KW (2018) Computational intelligence approach for modeling hydrogen production: a review. Eng Appl Comput Fluid Mech 12(1):438–458Google Scholar
  6. 6.
    AlKanj L, ElBeaino W, ElHajj AM, Dawy Z (2016) Optimized joint cell planning and BS ON/OFF switching for LTE networks. Wirel Commun Mob Comput 16(12):1537–1555CrossRefGoogle Scholar
  7. 7.
    Dolfi M, Cavdar C, Morosi S, Piunti P, Zander J, Del Re E (2017) On the trade-off between energy saving and number of switchings in green cellular networks. Trans Emerg Telecommun Technol 28(11):e3193CrossRefGoogle Scholar
  8. 8.
    Ghazzai H, Yaacoub E, Alouini MS, Abu-Dayya A (2014) Optimized smart grid energy procurement for LTE networks using evolutionary algorithms. IEEE Trans Veh Technol 63(9):4508–4519CrossRefGoogle Scholar
  9. 9.
    Sachan R, Saxena N (2014) Clustering based power management for green LTE networks. In: IEEE international conference on computer communication and informatics (ICCCI), pp 1–3Google Scholar
  10. 10.
    Shams AB, Jahid A, Hossain MF (2017) A CoMP based LTE—a simulator for green communications. In: IEEE international conference on wireless communications, signal processing and networking (WiSPNET), March 22 2017, pp 1751–1756Google Scholar
  11. 11.
    Jahid A, Shams AB, Hossain MF (2017) Energy efficiency of JT CoMP based green powered LTE—a cellular networks. In: ieee international conference on wireless communications, signal processing and networking (WiSPNET), March 22 2017, pp 1739–1745Google Scholar
  12. 12.
    Challita U, Dawy Z, Turkiyyah G, Naoum-Sawaya J (2016) A chance constrained approach for LTE cellular network planning under uncertainty. Comput Commun 73:34–45CrossRefGoogle Scholar
  13. 13.
    Munoz P, Barco R, de la Bandera I (2015) Load balancing and handover joint optimization in LTE networks using fuzzy logic and reinforcement learning. Comput Netw 76:112–125CrossRefGoogle Scholar
  14. 14.
    Gabli M, Jaara EM, Mermri EB (2016) A possibilistic approach to UMTS base-station location problem. Soft Comput 20(7):2565–2575CrossRefGoogle Scholar
  15. 15.
    Zadeh L (1995) Discussion: probability theory and fuzzy logic are complementary rather than competitive. Technometrics 37(3):271–276CrossRefGoogle Scholar
  16. 16.
    Negoita C, Zadeh L, Zimmermann H (1978) Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets Syst 1(3–28):61–72MathSciNetGoogle Scholar
  17. 17.
    Dubois D, Prade H (1980) Fuzzy sets and systems. Academic Press, New YorkzbMATHGoogle Scholar
  18. 18.
    Katagiri H, Mermri EB, Sakawa M, Kato K, Nishizaki I (2005) A possibilistic and stochastic programming approach to fuzzy random MST problems. IEICE Trans Inf Syst 88(8):1912–1919CrossRefGoogle Scholar
  19. 19.
    Sakawa M (1993) Fuzzy sets and interactive multiobjective optimization, vol 1. Plenum Press, New YorkCrossRefGoogle Scholar
  20. 20.
    McCall J (2005) Genetic algorithms for modelling and optimisation. J Comput Appl Math 184:205222MathSciNetCrossRefGoogle Scholar
  21. 21.
    Holland JH (1962) Outline for a logical theory of adaptive systems. J Assoc Comput Mach (JACM) 9:297–314CrossRefGoogle Scholar
  22. 22.
    Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, BostonzbMATHGoogle Scholar
  23. 23.
    Auer G, Blume O, Giannini V (2012) Energy efficiency analysis of the reference systems, areas of improvements and target breakdown. In: INFSO-ICT-247733 EARTH (energy aware radio and network technologies). Technical reportGoogle Scholar
  24. 24.
    Koutitas G (2010) Low carbon network planning. In: IEEE European 2010 wireless conference (EW), April 12, pp 411–417Google Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.LANO Laboratory, ESTO-FSOUniversity Mohammed PremierOujdaMorocco
  2. 2.Department of Computer Science, LARI Laboratory, Faculty of ScienceUniversity Mohammed PremierOujdaMorocco
  3. 3.Department of Mathematics, Faculty of ScienceUniversity Mohammed PremierOujdaMorocco

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