Performance Analysis of Weighting Methods for Handover Decision in HetNets

Year 2025, Early View, 1 - 1

Hamidullah Riaz Sıtkı Öztürk Sultan Aldırmaz Çolak Ali Çalhan

https://doi.org/10.35378/gujs.1373452

Abstract

The increasing demand for data, driven by advancements in technology, requires expanding coverage and enhancing network capacity. This expansion presents certain challenges, such as unnecessary Handover (HO) and interference, which can lead to a degradation in Quality of Service (QoS). To provide better QoS, it is vital to precisely model the HO decision-making process with optimal cell selection ensuring service continuity with minimal disruption. This paper investigates the performance of Analytic Hierarchy Process (AHP), Entropy, Standard Deviation (STD), and Weighted Sum Model (WSM) comparatively, while considering attributes such as Reference Signal Received Power (RSRP), Signal-to-Interference-Plus-Noise Ratio (SINR), channel capacity, and cell capacity. Additionally, the Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) is utilized to rank candidate cells for HO decisions. The performance of the considered weighting methods has been analyzed in a dense Small Cell (SC) Long-Term Evolution-Advanced (LTE-A) Heterogeneous Network (HetNet) environment based on system Key Performance Indicators (KPIs) such as HO Rate (HOR), HO Failure (HOF), Radio Link Failure (RLF), and HO Ping-Pong (HOPP). The evaluations have shown a trade-off between the methods in different KPIs. The findings highlight the importance of the weighting methods on HO decision, considering the significance of the specific KPIs.

Keywords

Dense small cells, Heterogeneous networks, Handover, MADM, Weighting methods

References

• [1] ETSI TS., “LTE; Evolved Universal Terrestrial Radio Access (E-UTRA); Physical channels and modulation”, (2010).
• [2] 3GPP TR., “3rd Generation Partnership Project; Technical Specification Group Radio Access Network; Evolved Universal Terrestrial Radio Access (E-UTRA); Mobility Enhancements in Heterogeneous Networks”, (2012).
• [3] Hasan, M. K., Ismail, A. F., Abdalla, A. H., Abdullah, K., Ramli, H., Islam, S., and Saeed, R. A., “Inter-cell interference coordination in LTE-A HetNets: A survey on self-organizing approaches”, 2013 International Conference on Computing, Electrical and Electronic Engineering (ICCEEE), Khartoum, Sudan, (2013).
• [4] Saleem, S., and Haneef, M., “A review of techniques to avoid cross-tier and co-tier interference in femtocell networks”, Journal of Engineering Research, 4(3): 57–84, (2016).
• [5] ETSI TS., “LTE; Evolved Universal Terrestrial Radio Access and Evolved Universal Terrestrial Radio Access Network (EUTRAN): overall description”, (2011).
• [6] Saquib, N., Hossain, E., and Kim, D. I., “Fractional frequency reuse for interference management in LTE-Advanced HetNets”, IEEE Wireless Communication, 20(2): 113–122, (2013).
• [7] Fradi, N., Najeh, S., and Boujemaa, H., “Resource allocation in OFDMA networks with femto and macro-cells coexistence using Fractional Frequency Reuse (FFR)”, Fourth International Conference on Communications and Networking, ComNet-2014, Hammamet, Tunisia, (2014).
• [8] Davaslioglu, K., Coskun, C. C., and Ayanoglu, E., “Energy-efficient resource allocation for fractional frequency reuse in heterogeneous network”, IEEE Transactions on Wireless Communications, 14(10): 5484–5497, (2015).
• [9] Khan, S. A., Kavak, A., Kucuk, K., and Asshad, M., “A new fractional frequency reuse method for interference management in LTE-A HetNets”, 2019 27th Signal Processing and Communications Applications Conference (SIU), Sivas, Turkey, (2019).
• [10] Khan, S. A., Kavak, A., Aldirmaz Çolak, S., and Kucuk, K., “A novel fractional frequency reuse scheme for interference management in LTE-A HetNets”, IEEE Access, 7: 109662–109672, (2019).
• [11] Sorooshian, S., and Parsia, Y., “Modified weighted sum method for decisions with altered sources of information”, Mathematics and Statistics, 7(3): 57–60, (2019).
• [12] Rao, R. V., Improved Multiple Attribute Decision Making Methods. In Decision Making in Manufacturing Environment Using Graph Theory and Fuzzy Multiple Attribute Decision Making Methods, Volume 2, Springer London, 7–39, (2013).
• [13] Alinezhad, A., and Khalili, J., New Methods and Applications in Multiple Attribute Decision Making (MADM), Springer, Volume 277, Springer Cham, 199–203, (2019).
• [14] Yeh, C-H., “A problem-based selection of multi-attribute decision-making methods”, International Transactions in Operational Research, 9(2): 169–181, (2002).
• [15] Bari, F., and Leung, V. C., “Automated network selection in a heterogeneous wireless network environment”, IEEE Network, 21(1): 34–40, (2007).
• [16] Bakmaz, B., Bojkovic, Z., and Bakmaz, M., “Network selection algorithm for heterogeneous wireless environment”, 2007 IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications, Athens, Greece, (2007).
• [17] Sgora, A., Vergados, D. D., and Chatzimisios, P., “An access network selection algorithm for heterogeneous wireless environments”, The IEEE symposium on Computers and Communications, Riccione, Italy, (2010).
• [18] Zhang, T., Liu, H., Zhou, Y., Migue, L-B., Lim, E., Ma, F., and Yu, L., “A network selection optimization algorithm based on AHP and TOPSIS in heterogeneous network environment”, 2021 2nd International Conference on Electronics, Communications and Information Technology (CECIT), Sanya, China, (2021).
• [19] Chen, X., Suh, Y. H., Kim, S. W., and Youn, H. Y., “Reducing connection failure in mobility management for LTE HetNet using MCDM algorithm”, 2015 IEEE/ACIS 16th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), Takamatsu, Japan, (2015).
• [20] Alhabo, M., and Zhang, L., “Multi-criteria handover using modified weighted TOPSIS methods for heterogeneous networks”, IEEE Access, 6: 40547–40558, (2018). [21] ETSI TR., “Evolved Universal Terrestrial Radio Access (E-UTRA); Radio Frequency (RF) system scenarios”, (2020).
• [22] Bouras, C., Kokkinos, V., Papazois, A., and Tseliou, G., “Fractional frequency reuse in integrated femtocell/macrocell environments”, International Conference on Wired/Wireless Internet Communication, Berlin, Germany, (2013).
• [23] Abdullahi, S. U., Liu, J., Huang, C., and Zhang, X., “Enhancing throughput performance in LTE-Advanced HetNets with buffered fractional frequency reuse”, 2016 Eighth International Conference on Ubiquitous and Future Networks (ICUFN), Vienna, Austria, (2016).
• [24] Lee, P., Lee, T., Jeong, J., and Shin, J., “Interference management in LTE femtocell systems using fractional frequency reuse”, 2010 The 12th International Conference on Advanced Communication Technology (ICACT), Phoenix Park, Korea, (2010).
• [25] Alhabo, M., Zhang, L., and Nawaz, N., “GRA-based handover for dense small cells heterogeneous networks”, IET Communications, 13(13): 1928–1935, (2019).
• [26] Chakraborty, S., and Yeh, C-H., “A simulation comparison of normalization procedures for TOPSIS”, 2009 International Conference on Computers & Industrial Engineering, Troyes, France, (2009).
• [27] Saaty, R.W., “The analytic hierarchy process—what it is and how it is used”, Mathematical Modelling, 9(3–5): 161–176, (1987).
• [28] Brunelli, M., Introduction to the Analytic Hierarchy Process, Springer, Cham, (2015).
• [29] Wedley, W. C., “Consistency prediction for incomplete AHP matrices”, Mathematical and Computer Modelling, 17(4–5): 151–161, (1993).
• [30] Shipley, M. F., Korvin, A., and Obid, R., “A decision making model for multi-attribute problems incorporating uncertainty and bias measures”, Computers & Operations Research, 18(4): 335–342, (1991).
• [31] Wang, Y-M., and Luo, Y., “Integration of correlations with standard deviations for determining attribute weights in multiple attribute decision making”, Mathematical and Computer Modelling, 51(1–2): 1–12, (2010).
• [32] Ben-Mubarak, M. A., Ali, B. M., Noordin, N. K., Ismail, A., and Ng, C. K., Selected Topics in WiMAX, InTech, 85–102, (2013).
• [33] Straccia, U., “Multi criteria decision making in fuzzy description logics: A first step”, International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, Berlin, Germany, (2009).
• [34] Hwang, C-L., and Masud, A. S. Md., Multiple Objective Decision Making — Methods and Applications: A State-of-the-Art Survey, Springer, Berlin, 1-183, (1979).