This study introduces a groundbreaking method for predicting network quality in LTE and 5G environments using only GPS data, focusing on pinpointing specific locations within a designated area to determine network quality as either good or poor. By leveraging machine learning algorithms, we have successfully demonstrated that geographical location can be a key indicator of network performance. Our research involved initially classifying network quality using traditional signal strength metrics and then shifting to rely exclusively on GPS coordinates for prediction. Employing a variety of classifiers, including Decision Tree, Random Forest, Gradient Boosting and K-Nearest Neighbors, we uncovered notable correlations between location data and network quality. This methodology provides network operators with a cost-effective and efficient tool for identifying and addressing network quality issues based on geographic insights. Additionally, we explored the potential implications of our study in various use cases, including healthcare, education, and urban industrialization, highlighting its versatility across different sectors. Our findings pave the way for innovative network management strategies, especially critical in the contexts of both LTE and the rapidly evolving landscape of 5G technology.

中文翻译：

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利用GPS数据预测指定区域的网络质量


本研究介绍了一种仅使用 GPS 数据来预测 LTE 和 5G 环境中网络质量的突破性方法，重点是精确定位指定区域内的特定位置，以确定网络质量的好坏。通过利用机器学习算法，我们成功证明地理位置可以成为网络性能的关键指标。我们的研究首先使用传统的信号强度指标对网络质量进行分类，然后转向完全依赖 GPS 坐标进行预测。通过采用各种分类器，包括决策树、随机森林、梯度提升和 K 最近邻，我们发现了位置数据和网络质量之间的显着相关性。该方法为网络运营商提供了一种经济高效的工具，用于根据地理洞察来识别和解决网络质量问题。此外，我们还探讨了我们的研究在各种用例中的潜在影响，包括医疗保健、教育和城市工业化，强调了其在不同领域的多功能性。我们的研究结果为创新网络管理策略铺平了道路，在 LTE 和快速发展的 5G 技术环境中尤其重要。