Network dimensioning is a critical task for cellular operators to avoid degraded user experience and unnecessary upgrades of network resources with changing mobile traffic patterns. For this purpose, smart network planning tools require accurate cell and user capacity estimates. In these tools, throughput is often used as a capacity metric due to its close relationship with user satisfaction. In this work, a comprehensive analysis is carried out to compare different well-known Supervised Learning (SL) algorithms for estimating cell and user throughput in the DownLink in busy hours from radio measurements collected on a cell basis in the Operation Support System (OSS). The considered SL approaches include random forest, shallow multi-layer perceptron, support vector regression and k-nearest neighbors. Such algorithms are compared with classical multiple linear regression and deep learning approaches considered in previous works. All these algorithms are tested in two radio access technologies: High Speed DownLink Packet Access (HSDPA) and Long Term Evolution (LTE). To this end, two datasets with the most relevant performance indicators per technology are collected from live cellular networks. Results show that non-deep SL algorithms are the most appropriate option for applications with storage constraints, such as network planning tools, since they provide a higher accuracy with reduced datasets.

中文翻译：

---

通过监督学习从无线电网络性能统计中估计杆容量

网络规模是蜂窝运营商的一项关键任务，以避免随着移动流量模式的变化而降低用户体验和不必要的网络资源升级。为此，智能网络规划工具需要准确的小区和用户容量估计。在这些工具中，吞吐量通常被用作容量指标，因为它与用户满意度密切相关。在这项工作中，进行了一项综合分析，以比较不同著名的监督学习 (SL) 算法，这些算法用于根据运营支持系统 (OSS) 中基于小区收集的无线电测量来估计繁忙时段下行链路中的小区和用户吞吐量. 考虑的 SL 方法包括随机森林、浅层多层感知器、支持向量回归和 k 最近邻。此类算法与之前工作中考虑的经典多元线性回归和深度学习方法进行了比较。所有这些算法都在两种无线接入技术中进行了测试：高速下行链路分组接入 (HSDPA) 和长期演进 (LTE)。为此，从实时蜂窝网络中收集了两个与每种技术最相关的性能指标的数据集。结果表明，非深度 SL 算法是具有存储限制的应用程序（例如网络规划工具）的最合适选择，因为它们在减少数据集的情况下提供更高的准确性。从实时蜂窝网络中收集了每种技术具有最相关性能指标的两个数据集。结果表明，非深度 SL 算法是具有存储限制的应用程序（例如网络规划工具）的最合适选择，因为它们在减少数据集的情况下提供更高的准确性。从实时蜂窝网络中收集了每种技术具有最相关性能指标的两个数据集。结果表明，非深度 SL 算法是具有存储限制的应用程序（例如网络规划工具）的最合适选择，因为它们在减少数据集的情况下提供更高的准确性。