http://iet.metastore.ingenta.com
1887

Analysis of traffic offload using multi-attribute decision making technique in heterogeneous shared networks

Analysis of traffic offload using multi-attribute decision making technique in heterogeneous shared networks

For access to this article, please select a purchase option:

Buy eFirst article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Networks — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Over the years, the authors have witnessed tremendous growth in mobile data traffic. To address the capacity requirements, the mobile network operators (MNOs) are adding more radio nodes and spectrum layers. The spectrum layers vary in quality of service (QoS), utilisation and usage fee. To save operational costs, the MNOs could share their radio networks. The MNOs, therefore, should consider spectrum availability, link utilisation, link usage costs, traffic handling priorities and QoS for selecting the Donor link for traffic offload. In this study, the traffic offload scenario with multiple Donor links from different MNOs is analysed. The authors define the relevant queuing parameters for Donor link selection. Two hybrid multi-attribute decision-making (MADM) techniques, namely, (a) analytical hierarchical process (AHP) with grey relational analysis (GRA) technique and (b) entropy with range of values (RoV) technique for dynamic Donor link selection in traffic offload scenario and assess their benefits were compared. The authors use IP network measurements to derive the packet service time. It is observed that both the techniques Entropy-RoV and AHP-GRA align well with the dynamism of the network, though Entropy-RoV method performed without any pre-configuration. The AHP-GRA technique requires pre-configuration of parameters and when configured suitably, it can perform as a load equalisation technique.

References

    1. 1)
      • 1. Cisco visual networking Index: global Mobile data traffic forecast update, 2016–2021’. Available at http://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/mobile-white-paper-c11-520862.html, accessed 29 May 2017.
    2. 2)
      • 2. Nokia mobile broadband India traffic Index (MBiT index) 2018’. Available at https://networks.nokia.com/in/mbit-index, accessed 15 August 2018.
    3. 3)
      • 3. 3GPP TR 36.889V13.0.0: ‘Study on licensed-assisted access to unlicensed spectrum’, 2015.
    4. 4)
      • 4. MFA TS MF.202V1.0.1: ‘Architecture for neutral host network access mode – stage 2’, 2017.
    5. 5)
      • 5. Irnich, T., Walke, B.H.: ‘Capacity dimensioning to meet delay percentile requirements’, in Takagi, H., Walke, B.H. (Eds.): ‘Spectrum requirement planning in wireless communications: model and methodology for IMT-advanced’ (John Wiley & Sons, Hoboken, NJ, USA., 2008), pp. 167192.
    6. 6)
      • 6. Cooperative association for internet data analysis, trace statistics for CAIDA’. Available at https://www.caida.org/data/passive/trace_stats/chicago-A/2016/equinix-chicago.dirA.20160317-130000.UTC.df.txt, accessed 29 May 2017.
    7. 7)
      • 7. Holma, H., Toskala, A.: ‘LTE for UMTS – OFDMA and SC-FDMA based radio access’ (John Wiley & Sons Ltd., Hoboken, NJ, USA., 2009), pp. 241242.
    8. 8)
      • 8. 3GPP TR 32.425V14.1.0: ‘Performance measurements evolved universal terrestrial radio access network (E-UTRAN)’, 2016.
    9. 9)
      • 9. Whaiduzzaman, M., Gani, A., Anuar, N.B., et al: ‘Cloud service selection using multicriteria decision analysis’, Scientific World J., 2014, 2014, Article ID: 49375, pp. 110.
    10. 10)
      • 10. Stevens-Navarro, E., Wong, V. W. S.: ‘Comparison between vertical handoff decision algorithms for heterogeneous wireless networks’. IEEE 63rd Vehicular Technology Conf., Melbourne, Vic., 2006, pp. 947951.
    11. 11)
      • 11. Wang, L., Kuo, G. S.: ‘Mathematical modeling for network selection in heterogeneous wireless networks – a tutorial’, IEEE Commun. Surv. Tutor., 2013, 15, (1), pp. 271292.
    12. 12)
      • 12. Saaty, T.L.: ‘How to make a decision: the analytic hierarchy process’, Interfaces. (Providence), 1994, 24, (6), pp. 1943.
    13. 13)
      • 13. Deng, J.L.: ‘Introduction to grey system theory’, J. Grey Syst., 1989, 1, (1), pp. 124.
    14. 14)
      • 14. Shannon, C. E.: ‘A mathematical theory of communication’, ACM SIGMOBILE Mob. Comput. Commun. Rev., 2001, 5, (1), pp. 355.
    15. 15)
      • 15. Rao, R. V.: ‘Decision making in the manufacturing environment: using graph theory and fuzzy multiple attribute decision making methods’, Springer Sci. Bus. Media, (Springer, London, UK, 2007).
    16. 16)
      • 16. Yakowitz, D. S., Lane, L. J., Szidarovszky, F.: ‘Multi-attribute decision making: dominance with respect to an importance order of the attributes’, Appl. Math. Comput., 1993, 54, (2–3), pp. 167181.
    17. 17)
      • 17. Gustafsson, E., Jonsson, A.: ‘Always best connected’, IEEE Wirel. Commun., 2003, 1, (1), pp. 4955.
    18. 18)
      • 18. Song, Q., Jamalipour, A.: ‘A network selection mechanism for next generation networks’. IEEE Int. Conf. on Communications, Seoul, South Korea, May 2005.
    19. 19)
      • 19. Liu, Y., Zhou, X., Ren, S.: ‘Peer selection in mobile P2P networks based on AHP and GRA’. 2012 18th IEEE Int. Conf. on Networks (ICON), Singapore, 2012.
    20. 20)
      • 20. Dudnikova, A., Dini, P., Giupponi, L., et al: ‘Multi-criteria decision for small cell switch off in ultra-dense LTE networks’. 2015 13th Int. Conf. on Telecommunications, Graz, Austria, 2015, pp. 18.
    21. 21)
      • 21. Fu, J., Wu, J., Zhang, J., et al: ‘A novel AHP and GRA based handover decision mechanism in heterogeneous wireless networks’, In Zhu, R., Zhang, Y., Liu, B., et al (Eds.): Information Computing and Applications. ICICA 2010. Lecture Notes in Computer Science, vol 6377, (Springer, Berlin, Heidelberg).
    22. 22)
      • 22. Khan, M., Han, K.: ‘An optimized network selection and handover triggering scheme for heterogeneous self-organized wireless networks’, Math. Probl. Eng., 2014, 2014, pp. 111.
    23. 23)
      • 23. Lei, K., Wang, J., Yuan, J.: ‘An entropy-based probabilistic forwarding strategy in named data networking’. IEEE Int. Conf. on Communications, London, UK, 2015, pp. 56655671.
    24. 24)
      • 24. Hajkowicz, S., Higgins, A.: ‘A comparison of multiple criteria analysis techniques for water resource management’, Eur. J. Oper. Res., 2008, 184, (1), pp. 255265.
    25. 25)
      • 25. Madic, M., Radovanovic, M., Manic, M.: ‘Application of the ROV method for the selection of cutting fluids’, Decis. Sci. Lett., 2016, 5, (2), pp. 245254.
    26. 26)
      • 26. Isik, A. T., Adali, E. A.: ‘The decision-making approach based on the combination of entropy and ROV methods for the apple selection problem’, Eur. J. Interdiscip. Stud., 2017, 8, (1), pp. 8086.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-net.2018.5139
Loading

Related content

content/journals/10.1049/iet-net.2018.5139
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address