Wireless local area networks (WLANs) empowered by IEEE 802.11 (WiFi) hold a dominant position in providing Internet access thanks to their freedom of deployment and configuration as well as affordable and highly interoperable devices. The WiFi community is currently deploying WiFi 6 and developing WiFi 7, which will bring higher data rates, better multi-user and multi-AP support, and, most importantly, improved configuration flexibility. These technical innovations, including the plethora of configuration parameters, are making next-generation WLANs exceedingly complex as the dependencies between parameters and their joint optimization usually have a non-linear impact on network performance. The complexity is further increased in the case of dense deployments and coexistence in shared bands. While classic optimization approaches fail in such conditions, machine learning (ML) is well known for being able to handle complexity. Much research has been published on using ML to improve WiFi performance and solutions are slowly being adopted in existing deployments. In this survey, we adopt a structured approach to describing the various areas where WiFi can be enhanced using ML. To this end, we analyze over 200 papers in the field providing readers with an overview of the main trends. Based on this review, we identify both open challenges in each WiFi performance area as well as general future research directions.
翻译:由IEEE 802.11(WiFi)授权的无线局域网(WLANs)在提供因特网接入方面占据主导地位,因为其部署和配置自由以及负担得起和高互操作装置。WiFi社区目前正在部署WiFi 6和开发WiFi 7,这将带来更高的数据率、更好的多用户和多用户支持,而且最重要的是,将改进配置灵活性。这些技术创新,包括配置参数过多,正在使下一代WiFi网变得极为复杂,因为参数及其联合优化之间的依赖性通常对网络性能产生非线性影响。在共用带密集部署和共存的情况下,其复杂性进一步增大。虽然经典的优化方法在这类条件下失败,但机器学习(ML)为人所熟知,因为能够处理复杂性。关于使用ML来改进WiFi的绩效和解决方案的大量研究正在缓慢地得到采用。在本次调查中,我们采取了一种结构化的方法来描述可使用ML加强WiFi的各个领域。为此目的,我们分析外地200多份文件,在提供未来主要业绩趋势的公开概览。