Since the emergence of wireless communication networks, a plethora of research papers focus their attention on the quality aspects of wireless links. The analysis of the rich body of existing literature on link quality estimation using models developed from data traces indicates that the techniques used for modeling link quality estimation are becoming increasingly sophisticated. A number of recent estimators leverage ML techniques that require a sophisticated design and development process, each of which has a great potential to significantly affect the overall model performance. In this paper, we provide a comprehensive survey on link quality estimators developed from empirical data and then focus on the subset that use ML algorithms. We analyze ML-based LQE models from two perspectives. Firstly, we focus on how they address quality requirements that are important from the perspective of the applications they serve. Secondly, we analyze how they approach the standard design steps commonly used in the ML community. Having analyzed the scientific body of the survey, we review existing open-source datasets suitable for LQE research. Finally, we round up our survey with the lessons learned and design guidelines for ML-based LQE development and dataset collection.

中文翻译：

---

用于无线链路质量估计的机器学习：一项调查

自从无线通信网络出现以来，大量的研究论文都将注意力集中在无线链路的质量方面。对使用从数据跟踪开发的模型进行链路质量估计的大量现有文献的分析表明，用于对链路质量估计建模的技术正变得越来越复杂。许多最近的估算器利用 ML 技术，这些技术需要复杂的设计和开发过程，每个技术都有很大的潜力来显着影响整体模型性能。在本文中，我们对根据经验数据开发的链路质量估计器进行了全面调查，然后重点关注使用 ML 算法的子集。我们从两个角度分析基于 ML 的 LQE 模型。首先，我们专注于他们如何解决从他们所服务的应用程序的角度来看很重要的质量要求。其次，我们分析了他们如何处理 ML 社区中常用的标准设计步骤。在分析了调查的科学主体后，我们审查了适用于 LQE 研究的现有开源数据集。最后，我们总结了基于 ML 的 LQE 开发和数据集收集的经验教训和设计指南。