ns-3 Simulation Based Exploration of LTE Handover Optimization





Network Simulator 3 (ns-3), Handovers, Radio Link Failures, Throughput, Simulation Execution Manager (SEM), Bandit Algorithms, Gaussian Process Regression, Linear Regression


Network simulator (ns-3) is a reputed simulation platform for performance evaluation of cellular networks. In this work, we explore the use of ns-3 for tracking of successful handovers (HO) and handover failures and consequent impact on 4G LTE network throughput with the aim of discovering new analytical relations about HOs and new methods to optimize the resulting throughput. Decreased cell sizes in newer generation networks lead to increasing number of handovers and handover failures that have significant impact. We begin by reviewing analytical models in the literature that aim to predict number of HO and HO failures in terms of HO control and network parameters. We initially conduct a suite of exhaustive validation studies of such analytical models, based on the simulation execution manager (SEM) for ns-3 for parallelization. Via this, we discover new causal relations relating HO failures and choice of HO control parameters on network throughput. Based on these initial results, we next evaluate the application of Gaussian process regression for prediction of instantaneous network throughput and bandit algorithms as an effective mechanism to optimize throughput over time. The new relations discovered help better understand the impact of input handover control parameters on the number of handovers and handover failures allowing us to fine tune them. The new optimization and prediction methods discovered give good gains over baseline algorithms and help accurately predict throughput respectively.


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Andrews, J.G., Buzzi, S., Choi, W., Hanly, S.V., Lozano, A., Soong, A.C.K. and Zhang, J.C. (2014) What will 5g be? IEEE Journal on Selected Areas in Communications 32(6): 1065–1082. doi:10.1109/JSAC.2014.2328098.

Dimou, K.D., Wang, M., Yang, Y., Kazmi, M., Larmo, A., Pettersson, J., Müller, W. et al. (2009) Handover within 3gpp lte: Design principles and performance. 2009 IEEE 70th Vehicular Technology Conference Fall : 1–5.

Nguyen, M.T. and Kwon, S. (2020) Geometry-based analysis of optimal handover parameters for self-organizing networks. IEEE Transactions on Wireless Com-munications PP: 1–1. doi:10.1109/TWC.2020.2967668.

Chen, D., Liu, J., Huang, Z., Zhang, Z. and Wu, J. (2015) Theoretical analysis of handover failure and no handover rates for heterogeneous networks. In 2015 International Conference on Wireless Communications Signal Processing (WCSP): 1–5. doi:10.1109/WCSP.2015.7341299.

Lee, C., Cho, H.J., Song, S. and Chung, J. (2020) Prediction-based conditional handover for 5g mm-wave networks: A deep-learning approach. IEEE Vehicular Technology Magazine 15: 54–62.

Ericsson Blog (2020). This is the key to mobility robustness in 5g networks. https://www.ericsson.com/en/blog/2020/5/the-key-to-mobility-robustness-5g-networks.

Park, H., Lee, Y., Kim, T., Kim, B.C. and Lee, J.(2021) Zeus: Handover algorithm for 5g to achieve zero handover failure. ETRI Journal doi:10.4218/etrij.2020-0356.

Jiang, W. (2022) Graph-based deep learning for communication networks: A survey. Computer Communications 185: 40 - 54. doi:https://doi.org/10.1016/j.comcom.2021.12.015, URL https://www.sciencedirect.com/science/article/pii/S0140366421004874.

He, S., Xiong, S., Ou, Y., Zhang, J., Wang, J., Huang, Y. and Zhang, Y. (2021) An overview on the application of graph neural networks in wireless networks. IEEE Open Journal of the Communications Society PP: 1–1. doi:10.1109/OJCOMS.2021.3128637.

Yang, L., Cheng, M., Qu, J. and Chen, Z. (2022) Graphho: A graph-based handover optimization system for cellular networks. In 2022 International Symposium on Wireless Communication Systems (ISWCS): 1–6. doi:10.1109/ISWCS56560.2022.9940345.

Zhao, S., Jiang, X., Jacobson, G., Jana, R., Hsu, W. L., Rustamov, R., Talasila, M. et al. (2020) Cel-lular network traffic prediction incorporating han-dover: A graph convolutional approach. In 2020 17th Annual IEEE International Conference on Sens-ing, Communication, and Networking (SECON): 1–9. doi:10.1109/SECON48991.2020.9158437.

Baldo, N., Miozzo, M., Requena-Esteso, M. and Nin-Guerrero, J. (2011) An open source product-oriented lte network simulator based on ns-3. In Proceedings of the 14th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, MSWiM ’11 (New York, NY, USA: Association for Computing Machinery): 293–298. doi:10.1145/2068897.2068948, URL https://doi.org/10.1145/2068897.2068948.

Baldo, N., Requena-Esteso, M., Miozzo, M. and Kwan, R. (2013) An open source model for the simulation of lte handover scenarios and algorithms in ns-3. In Proceedings of the 16th ACM International Conference on Modeling, Analysis &; Simulation of Wireless and Mobile Systems, MSWiM ’13 (New York, NY, USA: Association for Computing Machinery): 289–298. doi:10.1145/2507924.2507940, URL https://doi.org/ 10.1145/2507924.2507940.

Magrin, D., Zhou, D. and Zorzi, M. (2019) A simulation execution manager for ns-3: Encouraging reproducibility and simplifying statistical analysis of ns-3 simulations: 121–125. doi:10.1145/3345768.3355942.

Herman, B., Baldo, N., Miozzo, M., Requena, M. and Ferragut, J. (2014) Extensions to lte mobility functions for ns-3. In Proceedings of the 2014 Workshop on Ns-3, WNS3 ’14 (New York, NY, USA: Association for Computing Machinery). doi:10.1145/2630777.2630779, URL https://doi.org/10.1145/2630777.2630779.

López-Pérez, D., Guvenc, I. and Chu, X. (2012) Theoretical analysis of handover failure and ping-pong rates for heterogeneous networks. In 2012 IEEE International Conference on Communications (ICC): 6774–6779. doi:10.1109/ICC.2012.6364722.

Vasudeva, K., Simsek, M., Lopez-Perez, D. and Guvenc, I. (2015) Impact of channel fading on mobility management in heterogeneous networks. doi:10.1109/ICCW.2015.7247509.

Marinescu, A., Macaluso, I. and Dasilva, L. (2017) System level evaluation and validation of the ns-3 lte module in 3gpp reference scenarios: 59–64. doi:10.1145/3132114.3132117.

Hendrawan, H., Zain, A. and Lestari, S. (2019) Performance evaluation of a2-a4-rsrq and a3-rsrp handover algorithms in lte network. Jurnal Elektronika dan Telekomunikasi 19: 64. doi:10.14203/jet.v19.64-74.

Lin, P.C., Casanova, L. and Fatty, B. (2016) Data-driven handover optimization in next generation mobile communication networks. Mobile Information Systems 2016: 1–11. doi:10.1155/2016/2368427.

Lee, Y., Shin, B., Lim, J. and Hong, D. (2010) Effects of time-to-trigger parameter on handover per-formance in son-based lte systems. In 2010 16th Asia-Pacific Conference on Communications (APCC): 492–496. doi:10.1109/APCC.2010.5680001.

Bae, H.D., Ryu, B. and Park, N.H. (2011) Analysis of handover failures in lte femtocell systems. In 2011 Australasian Telecommunication Networks and Applications Conference (ATNAC): 1–5. doi:10.1109/ATNAC.2011.6096636.

Legg, P., Hui, G. and Johansson, J. (2010) A simulation study of lte intra-frequency handover performance. In 2010 IEEE 72nd Vehicular Technology Conference - Fall: 1–


(2020) E-UTRA Radio Resource Control (RRC) protocol specification; Radio Resource Control (RRC); Protocol specification. Tech. Rep. TS 36.331, 3GPP Release 16.

(2020) E-UTRA Radio Resource Control (RRC) protocol specification; Requirements for support of radio resource management. Tech. Rep. TS 36.133, 3GPP Release 16.

Alcatel-Lucent, p.D. and Vodafone (2009) Simulation assumptions and parameters for FDD HeNB RF require-ment. Tech. Rep. R4-092042, 3GPP TSG RAN WG4.

URL https://github.com/sachinnUW/ns-3-dev/tree/lena-sim.




How to Cite

S. Nayak, “ns-3 Simulation Based Exploration of LTE Handover Optimization”, EAI Endorsed Trans Mob Com Appl, vol. 7, no. 4, p. e4, May 2023.