Integrated Grey Rational Analysis-Standard Deviation Method for Handover in Heterogeneous Networks

Authors: Mohanad Alhabo, Naveed Nawaz, Mahmoud Al-Faris

Abstract:

The dense deployment of small cells is a promising solution to enhance the coverage and capacity of the heterogeneous networks (HetNets). However, the unplanned deployment could bring new challenges to the network ranging from interference, unnecessary handovers and handover failures. This will cause a degradation in the quality of service (QoS) delivered to the end user. In this paper, we propose an integrated Grey Rational Analysis Standard Deviation based handover method (GRA-SD) for HetNet. The proposed method integrates the Standard Deviation (SD) technique to acquire the weight of the handover metrics and the GRA method to select the best handover base station. The performance of the GRA-SD method is evaluated and compared with the traditional Multiple Attribute Decision Making (MADM) methods including Simple Additive Weighting (SAW) and VIKOR methods. Results reveal that the proposed method has outperformed the other methods in terms of minimizing the number of frequent unnecessary handovers and handover failures, in addition to improving the energy efficiency.

Keywords: Energy efficiency, handover, HetNets, MADM, small cells.

References:

[1] A. M. Akhtar, X. Wang, and L. Hanzo, “Synergistic spectrum sharing in 5g hetnets: A harmonized sdn-enabled approach,” IEEE Communications Magazine, vol. 54, no. 1, pp. 40–47, 2016.
[2] X. Chu, D. L´opez-P´erez, Y. Yang, and F. Gunnarsson, Heterogeneous Cellular Networks: Theory, Simulation and Deployment. Cambridge University Press, 2013.
[3] G. T. 36.839, “Evolved universal terrestrial radio access (eutra); mobility enhancements in heterogeneous networks,” 2013.
[4] M. Alhabo and L. Zhang, “Unnecessary handover minimization in two-tier heterogeneous networks,” in Wireless On-demand Network Systems and Services (WONS), 2017 13th Annual Conference on. IEEE, 2017, pp. 160–164.
[5] M. Alhabo, L. Zhang, and N. Nawaz, “A trade-off between unnecessary handover and handover failure for heterogeneous networks,” in European Wireless 2017; 23th European Wireless Conference; Proceedings of. VDE, 2017.
[6] M. Alhabo, L. Zhang, and O. Oguejiofor, “Inbound handover interference-based margin for load balancing in heterogeneous networks,” in Wireless Communication Systems (ISWCS), 2017 International Symposium on. IEEE, 2017, pp. 1–6.
[7] K. Aghababaiyan and B. Maham, “Qos-aware downlink radio resource management in ofdma-based small cells networks,” IET Communications, vol. 12, no. 4, pp. 441–448, 2017.
[8] M. Yassin, S. Lahoud, M. Ibrahim, K. Khawam, D. Mezher, and B. Cousin, “Cooperative resource management and power allocation for multiuser ofdma networks,” IET Communications, vol. 11, no. 16, pp. 2552–2559, 2017.
[9] S. Wu, Z. Zeng, and H. Xia, “Coalition-based sleep mode and power allocation for energy efficiency in dense small cell networks,” IET Communications, vol. 11, no. 11, pp. 1662–1670, 2017.
[10] M. Alhabo and L. Zhang, “Load-dependent handover margin for throughput enhancement and load balancing in hetnets,” IEEE Access, vol. 6, pp. 67 718–67 731, 2018.
[11] A. Huszak and S. Imre, “Eliminating rank reversal phenomenon in gra-based network selection method,” in Communications (ICC), 2010 IEEE International Conference on. IEEE, 2010, pp. 1–6.
[12] H. A. Al-Kashoash, H. M. Amer, L. Mihaylova, and A. H. Kemp, “Optimization based hybrid congestion alleviation for 6lowpan networks,” IEEE Internet of Things Journal, 2017.
[13] L. Wang and G.-S. G. Kuo, “Mathematical modeling for network selection in heterogeneous wireless networksa tutorial,” IEEE Communications Surveys & Tutorials, vol. 15, no. 1, pp. 271–292, 2013.
[14] R. Tawil, G. Pujolle, and O. Salazar, “A vertical handoff decision scheme in heterogeneous wireless systems,” in Vehicular Technology Conference, 2008. VTC Spring 2008. IEEE. IEEE, 2008, pp. 2626–2630.
[15] C.-H. Yeh, “A problem-based selection of multi-attribute decision-making methods,” International Transactions in Operational Research, vol. 9, no. 2, pp. 169–181, 2002.
[16] L. Mohamed, C. Leghris, and A. Abdellah, “A hybrid approach for network selection in heterogeneous multi-access environments,” in New Technologies, Mobility and Security (NTMS), 2011 4th IFIP International Conference on. IEEE, 2011, pp. 1–5.
[17] M. Alhabo and L. Zhang, “Multi-criteria handover using modified weighted topsis methods for heterogeneous networks,” IEEE Access, vol. 6, pp. 40 547–40 558, 2018.
[18] J. Ariyakhajorn, P. Wannawilai, and C. Sathitwiriyawong, “A comparative study of random waypoint and gauss-markov mobility models in the performance evaluation of manet,” in Communications and Information Technologies, 2006. ISCIT’06. International Symposium on. IEEE, 2006, pp. 894–899.
[19] J. Zhang and G. De la Roche, Femtocells: technologies and deployment. John Wiley & Sons, 2011.
[20] Q. Europe, “Hnb and hnb-macro propagation models,” 3GPP R4–071617, Oct, 2007.
[21] G. L. St¨uber, Principles of mobile communication. Springer Science & Business Media, 2011.
[22] S. Singh and J. G. Andrews, “Rate distribution in heterogeneous cellular networks with resource partitioning and offloading,” in Global Communications Conference (GLOBECOM), 2013 IEEE. IEEE, 2013, pp. 3796–3801.
[23] Y.-M. Wang and Y. Luo, “Integration of correlations with standard deviations for determining attribute weights in multiple attribute decision making,” Mathematical and Computer Modelling, vol. 51, no. 1, pp. 1–12, 2010.
[24] MathWorks, “Counting the floating point operations (flops),” 2015.
[Online]. Available: https://uk.mathworks.com
[25] D. Lopez-Perez, I. Guvenc, and X. Chu, “Mobility management challenges in 3gpp heterogeneous networks,” IEEE Communications Magazine, vol. 50, no. 12, 2012.
[26] X. Xiao, X. Tao, Y. Jia, and J. Lu, “An energy-efficient hybrid structure with resource allocation in ofdma networks,” in Wireless Communications and Networking Conference (WCNC), 2011 IEEE. IEEE, 2011, pp. 1466–1470.