mca 23(4): e4

Research Article

ns-3 Simulation Based Exploration of LTE Handover Optimization

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  • @ARTICLE{10.4108/eetmca.v7i4.2967,
        author={Sachin Nayak},
        title={ns-3 Simulation Based Exploration of LTE Handover Optimization},
        journal={EAI Endorsed Transactions on Mobile Communications and Applications},
        volume={7},
        number={4},
        publisher={EAI},
        journal_a={MCA},
        year={2023},
        month={5},
        keywords={Network Simulator 3 (ns-3), Handovers, Radio Link Failures, Throughput, Simulation Execution Manager (SEM), Bandit Algorithms, Gaussian Process Regression, Linear Regression},
        doi={10.4108/eetmca.v7i4.2967}
    }
    
  • Sachin Nayak
    Year: 2023
    ns-3 Simulation Based Exploration of LTE Handover Optimization
    MCA
    EAI
    DOI: 10.4108/eetmca.v7i4.2967
Sachin Nayak1,*
  • 1: University of Washington
*Contact email: sachinn@uw.edu

Abstract

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.