论文标题
一项关于基于机器学习的无线网络性能改进的调查：PHY，MAC和网络层
A survey on Machine Learning-based Performance Improvement of Wireless Networks: PHY, MAC and Network layer
论文作者
论文摘要
本文提供了一项系统的全面调查，审查了针对基于机器学习的最新研究工作（ML）的无线网络性能改进，同时考虑了协议堆栈的所有层（PHY，MAC和网络）。首先，讨论了相关的工作和纸质贡献，然后为非机器学习专家提供有关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的方法来优化无线通信参数设置以实现改进网络服务质量（QOS）和体验质量（QOE）的作品进行了全面审查。我们首先将这些作品分类为：无线电分析，MAC分析和网络预测方法，然后在每种过程中进行子类别。最后，讨论了公开的挑战和更广泛的观点。
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.
