加纳移动运营商服务质量的数据驱动评估

IF 1.1 Q2 SOCIAL SCIENCES, INTERDISCIPLINARY
Bong Jun Choi, Suzana Brown, Nii Ayitey Komey
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引用次数: 0

摘要

移动服务的迅速普及增加了以数据为导向对服务质量进行监督的需求,然而从监管机构收集的数据集中获取洞察力仍具有挑战性。本研究展示了如何利用驾驶测试测量数据的丰富潜力来分析监管和政策决策。我们以加纳领先运营商 MTN 为重点,分析了电信监管机构为首都阿克拉提供的 4 年驾驶测试数据。我们评估了三个关键性能指标--覆盖范围、呼叫建立时间和语音质量。我们通过统计摘要、数据可视化和机器学习建模评估了服务质量趋势,并预测了语音质量得分。我们的分析表明,2019 年后服务质量不断下降,并发现轻梯度提升机算法对语音质量的预测准确率最高。总之,这项工作展示了监管机构如何利用大数据挖掘技术,利用庞大的数据集来评估不同时间和地域的网络状况,从而加强实地测量以进行监督。随着移动网络的发展,我们的方法和技术为基于证据的政策制定提供了模板,以维护消费者的服务质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A data-driven assessment of mobile operator service quality in Ghana

The rapid proliferation of mobile services has increased the need for data-driven oversight of service quality, yet deriving insights from regulator-collected datasets remains challenging. This study demonstrates techniques to tap the rich potential of drive test measurement data for analytical regulatory and policy decision-making. Focusing on leading operator MTN in Ghana, we analyzed 4 years of drive test data supplied by the telecom regulator for the capital city of Accra. Three key performance indicators were evaluated—coverage, call setup time, and speech quality. We assessed service quality trends through statistical summaries, data visualization, and machine learning modeling and predicted speech quality scores. Our analysis revealed deteriorating performance post-2019 and found that the light gradient boosting machine algorithm provided the highest accuracy predictions of speech quality. Overall, this work showcases how regulators can capitalize on vast datasets using big data mining techniques to evaluate network conditions over time and geography, enhancing field measurements for oversight. Our approach and techniques provide a template for evidence-based policy-making to uphold consumer service quality as mobile networks evolve.

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来源期刊
CiteScore
3.60
自引率
15.40%
发文量
51
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