ML-driven Radio Resource Management in Wireless Local Area Networks

由IEEE 802.11(Wi-Fi)支持的无线局域网(WLAN)在提供互联网接入方面占据主导地位，这是因为部署和配置(由于在未经许可的频段中操作)的自由以及经济实惠和高度可互操作的设备。然而，随着这些网络的密集化，在共享的未经许可的频谱中运行的无线局域网的计划外部署和分布式管理正在成为一个新的挑战。另一个挑战与技术创新有关，这使得这种技术的下一代变得极其复杂。具体地说，每一种旨在提高网络性能的新机制都带有过多的参数，必须正确配置这些参数才能达到最佳效果(标准中没有这种配置)。在大多数情况下，多个参数必须一起调优，这不是一项微不足道的任务，因为参数之间的依赖关系及其联合优化对网络性能具有高度非线性的影响。在共存网络的情况下，复杂性水平进一步增加，其中必须在服务于具有不同服务质量要求的各种应用的多个节点上设置不同的参数。事实上，未来一代的WLAN预计不仅要适应高吞吐量，而且要适应低延迟和高可靠性的流量。综上所述，下一代无线局域网的问题在于，传统的无线资源管理(RRM)算法不能保证在越来越多的配置选项下以计划外部署和分布式管理为特征的一系列场景具有合理的性能，所有这些因素使得ML算法非常适合现代网络，即它可以提供精确的估计模型，帮助解决现有问题，并鼓励新的解决方案可能导致突破。然而，应用最大似然算法来解决现代无线网络的问题提出了一定的挑战。例如，它需要定义RRM问题的环境状态(即观察空间)、动作空间以及奖励函数，这并不是一个明显的任务，但对学习过程和网络性能有关键影响。在以许可频段运营的5G网络中，这一挑战和其他与ML相关的挑战正在慢慢克服。然而，由于无线局域网的特点不同，为5G设计的解决方案将不会直接适用于无线局域网。首先，5G采用集中管理的方式，精心规划部署。同时，无线局域网使用分布式管理方法，在大多数情况下，部署是计划外的和混乱的。其次，5G在许可频段运行，没有外部干扰，而WLAN在共享频段运行，它们彼此干扰以及与其他技术的设备干扰。ML4WIFI项目将解决这些问题。

Wireless local area networks (WLANs) empowered by IEEE 802.11 (Wi-Fi) hold a dominant position in providing Internet access due to freedom of deployment and configuration (thanks to operating in unlicensed bands) and affordable and highly interoperable devices. However, the unplanned deployment and distributed management of WLANs operating in shared unlicensed spectrum is becoming an emerging challenge with the densification of these networks. Another challenge is related to technical innovations, which are making the next-generation of this technology exceedingly complex. Specifically, each new mechanism, designed to improve network performance, comes with a plethora of parameters which have to be properly configured to achieve the best results (and this configuration is left out of the standard). In most cases, multiple parameters have to be tuned together, which is a non-trivial task as the dependencies between parameters and their joint optimization have a highly non-linear impact on network performance. The level of complexity is further increased in the case of coexisting networks, where diverse parameters have to be set across multiple nodes that serve various applications with different QoS requirements. Indeed, future WLAN generations are anticipated to accommodate not only high throughput but also low latency and high-reliability traffic. In summary, the problem of next-generation WLANs is that traditional radio resource management (RRM) algorithms fail to guarantee a reasonable level of performance across a range of scenarios characterized by unplanned deployments and distributed management under the increasing number of configuration options.All these factors make ML algorithms a perfect fit for modern networking, i.e., it can provide estimated models with tunable accuracy, help in tackling existing problems, and encourage new solutions potentially leading to breakthroughs. However, the application of ML algorithms to solve the problems of modern wireless networks poses certain challenges. For example, it requires the definition of the environment state (i.e., observation space), the action space, as well as the reward function for the RRM problem, which is not an obvious task but has a critical impact on the learning process and network performance. This and other ML-related challenges are being slowly overcome in 5G networks operating in licensed bands. Nevertheless, solutions designed for 5G will not be directly applicable to WLANs due to their characteristic differences. First, 5G uses a centralized management approach with carefully planned deployment. Meanwhile, WLANs use a distributed management approach where the deployment is in most cases unplanned and chaotic. Second, 5G operates in licensed bands, with no outside interference, whereas WLANs operate in shared bands where they interfere with each other as well as with devices of other technologies. The ML4WIFI project will address those issues.