This paper provides a systematic and comprehensive survey that reviews the latest research efforts focused on machine learning (ML) based performance improvement of wireless networks, while considering all layers of the protocol stack (PHY, MAC and network). First, the related work and paper contributions are discussed, followed by providing the necessary background on data-driven approaches and machine learning for non-machine learning experts to understand all discussed techniques. Then, a comprehensive review is presented on works employing ML-based approaches to optimize the wireless communication parameters settings to achieve improved network quality-of-service (QoS) and quality-of-experience (QoE). We first categorize these works into: radio analysis, MAC analysis and network prediction approaches, followed by subcategories within each. Finally, open challenges and broader perspectives are discussed.

中文翻译：

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基于机器学习的无线网络性能改进调查：PHY、MAC 和网络层

本文提供了系统而全面的调查，回顾了基于机器学习 (ML) 的无线网络性能改进的最新研究成果，同时考虑了协议栈的所有层（PHY、MAC 和网络）。首先，讨论相关工作和论文贡献，然后为非机器学习专家提供数据驱动方法和机器学习的必要背景，以了解所有讨论的技术。然后，对采用基于 ML 的方法优化无线通信参数设置以实现改进的网络服务质量 (QoS) 和体验质量 (QoE) 的工作进行了全面回顾。我们首先将这些工作分类为：无线电分析、MAC 分析和网络预测方法，然后是每个类别中的子类别。