Mobile Networks and Applications

, Volume 23, Issue 6, pp 1682–1692 | Cite as

Energy Efficiency and Superlative TTT for Equitable RLF and Ping Pong in LTE Networks

  • Kapil Kanwal
  • Ghazanfar Ali SafdarEmail author


Data hungry users engage radio resources over long periods of time thus resulting into higher energy consumption by Base Stations (BSs). Mobile operators’ operational expenditure (OPEX) is directly affected by augmented electricity bills due to increased power consumption, thereby ensuing reduced economic and environmental benefits, i.e. profitability of vendors and green communication accordingly. This work provides performance analysis of our proposed reduced early handover (REHO) scheme which results in increased energy efficiency. Impact of reduced energy consumption is shown on OPEX, as well as greener aspects are investigated by inclusion of real life commercial tariffs adopted by one of the mobile operators in the UK. Performance analysis revealed that varying time to trigger (TTT) values significantly impact radio link failure (RLF), ping pong effect as well as call drop ratio (CDR) and Handover ratio (HOR), at changing users’ velocities. Paper investigates and provides a very useful insight for superlative value of TTT for unbiased RLF and Ping Pong, which can help vendors not only to achieve increased energy efficiency, but also maintain other salient performance parameters within acceptable limits. The work also achieves the fact that the time difference in terms of transmission time intervals (TTIs) for reduced early handover in REHO, always remain the same irrespective of the value of TTT, thus ensuring that REHO continuously achieves increased energy efficiency compared to LTE standard.


Handover OPEX Energy efficiency LTE 


  1. 1.
    Ghosh A, Ratasuk R, Mondal B, Mangalvedhe N, Thomas T (2010) LTE-advanced: next-generation wireless broadband technology [Invited Paper]. IEEE Wirel Commun 17(3):10–22CrossRefGoogle Scholar
  2. 2.
    Mahapatra R, Nijsure Y, Kaddoum G, Ul Hassan N, Yuen C (2016) Energy efficiency tradeoff mechanism towards wireless green communication: a survey. IEEE Commun Surv Tutor 18(1):686–705 First quarterCrossRefGoogle Scholar
  3. 3.
    Ismail M, Zhuang W (2011) Network cooperation for energy saving in green radio communications. IEEE Wirel Commun 18(5):76–81CrossRefGoogle Scholar
  4. 4.
    K. Liu, J. He, J. Ding, Y. Zhu and Z. Liu (2013) Base station power model and application for energy efficient LTE. Communication Technology (ICCT), 2013 15th IEEE International Conference on, Guilin. 86–92.Google Scholar
  5. 5.
    Katsinis G, Tsiropoulou EE, Papavassiliou S (2016) Joint resource block and power allocation for interference Management in Device to device underlay cellular networks: a game theoretic approach. Springer Mobile Net Appl.Google Scholar
  6. 6.
    Han C, Armour S (2011) Energy efficient radio resource management strategies for green radio. Commun IET 5(18):2629–2639CrossRefGoogle Scholar
  7. 7.
    Saadat S, Chen D, Jiang T (2016) QoS guaranteed resource allocation scheme for cognitive femtocells in LTE heterogeneous networks with universal frequency reuse. Mob Netw Appl 21(6):930–942CrossRefGoogle Scholar
  8. 8.
    Oh E, Son K, Krishnamachari B (2013) Dynamic base station switching-on/off strategies for green cellular networks. IEEE Trans Wirel Commun 12(5):2126–2136CrossRefGoogle Scholar
  9. 9.
    Mehta M, Akhtar N, Karandikar A (2015) Impact of HandOver parameters on mobility performance in LTE HetNets. 2015 Twenty First National Conference on Communications (NCC), Mumbai, pp 1–6Google Scholar
  10. 10.
    Muñoz P, Barco R, de la Bandera I (2013) On the Potential of Handover Parameter Optimization for Self-Organizing Networks. IEEE Trans Veh Technol 62(5):1895–1905CrossRefGoogle Scholar
  11. 11.
    Lee Y, Shin B, Lim J, Hong D (2010) Effects of time-to-trigger parameter on handover performance in SON-based LTE systems. 2010 16th Asia-Pacific Conference on Communications (APCC), Auckland, pp 492–496Google Scholar
  12. 12.
    Andrews JG, Claussen H, Dohler M, Rangan S, Reed MC (2012) Femtocells: past, present, and future. Select Areas Commun IEEE J 30(3):497–508CrossRefGoogle Scholar
  13. 13.
    GA Safdar, K Kanwal (2017) Euclidean geometry axioms assisted target cell boundary approximation for improved energy efficacy in LTE systems. IEEE Syst J (99):1–9Google Scholar
  14. 14.
    Hanzo L II, Mostafavi SM, Tafazolli R (2008) Connectivity-related properties of mobile nodes obeying the random walk and random waypoint mobility models. Vehicular Technology Conference, 2008. VTC Spring 2008. IEEE, Singapore, pp 133–137Google Scholar
  15. 15.
    Bettstetter C, Resta G, Santi P (2003) The node distribution of the random waypoint mobility model for wireless ad hoc networks. IEEE Trans Mob Comput 2(3):257–269CrossRefGoogle Scholar
  16. 16.
    3GPP. Evolved Universal Terrestrial Radio Access (E-UTRA) and Evolved Universal Terrestrial Radio Access Networks (E UTRAN): Overall description. TS 36.300, V10.4.0.Google Scholar
  17. 17.
    3GPP. 3rd Generation Partnership Project; Technical Specification Group Radio Access Networks; Radio Frequency (RF) system scenarios. TR 25.942, V9.0.0Google Scholar
  18. 18.
    Verbrugge S et al (2006) Methodology and input availability parameters for calculating OpEx and CapEx costs for realistic network scenarios. J Opt Netw 5(6):509–520CrossRefGoogle Scholar
  19. 19.
    Verbrugge S et al. (2005) Modeling operational expenditures for telecom operators. Proc 9th Conf Optic Netw Des Model 455–466Google Scholar
  20. 20.
    Knoll TM (2014) A combined CAPEX and OPEX cost model for LTE networks. Telecommunications Network Strategy and Planning Symposium (Networks), 2014 16th International, Funchal, pp 1–6Google Scholar
  21. 21.
    Petrut I, Otesteanu M, Balint C, Budura G (2015) HetNet handover performance analysis based on RSRP vs. RSRQ triggers. 2015 38th International Conference on Telecommunications and Signal Processing (TSP), Prague, pp 232–235Google Scholar
  22. 22.
    Guo W, O'Farrell T (2012) Capacity-energy-cost tradeoff in small cell networks. Vehicular Technology Conference (VTC Spring), 2012 IEEE 75th, Yokohama, pp 1–5Google Scholar
  23. 23.
    Saxena N, Sahu BJR, Han YS (2014) Traffic-Aware Energy Optimization in Green LTE Cellular Systems. IEEE Commun Lett 18(1):38–41CrossRefGoogle Scholar
  24. 24.
    Sovacool K (2008) Valuing the greenhouse gas emissions from nuclear power: A critical survey. Elsevier Energy Policy 36:2940–2953Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer ScienceUniversity of BedfordshireLutonUK

Personalised recommendations