ML-driven Radio Resource Management in Wireless Local Area Networks

无线局域网中机器学习驱动的无线电资源管理

• 批准号: 465309697
• 负责人: Professor Dr.-Ing. Falko Dressler
• 金额: --
• 依托单位: Fachgebiet Telekommunikationsnetze (TKN)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/465309697?language=en
• 关键词: ML; driven; Radio; Resource; Management

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.

由IEEE 802.11（Wi-Fi）授权的无线局域网（WLAN）在提供互联网接入方面占据主导地位，这是由于部署和配置的自由度（由于在未授权频带中操作）以及负担得起且高度可互操作的设备。然而，随着这些网络的密集化，在共享的未许可频谱中操作的WLAN的无计划部署和分布式管理正在成为一个新兴的挑战。另一个挑战与技术创新有关，这使得下一代技术变得非常复杂。具体来说，每一种旨在提高网络性能的新机制都带有过多的参数，这些参数必须正确配置才能实现最佳结果（而这种配置不在标准中）。在大多数情况下，多个参数必须一起调整，这是一项重要的任务，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，其中必须跨服务于具有不同QoS要求的各种应用的多个节点设置不同的参数。事实上，未来的WLAN一代预计不仅要适应高吞吐量，而且还要适应低延迟和高可靠性业务。总之，下一代WLAN的问题在于，传统的无线电资源管理（RRM）算法无法在以不断增加的配置选项下的无计划部署和分布式管理为特征的一系列场景中保证合理的性能水平。所有这些因素使得ML算法完美地适合现代网络，即，它可以提供具有可调精度的估计模型，帮助解决现有问题，并鼓励可能导致突破的新解决方案。然而，ML算法的应用，以解决现代无线网络的问题提出了一定的挑战。例如，它需要定义环境状态（即，观察空间）、动作空间以及RRM问题的奖励函数，这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。在授权频段运行的5G网络中，这一挑战和其他ML相关挑战正在慢慢克服。然而，为5G设计的解决方案由于其特性差异而无法直接适用于WLAN。首先，5G使用集中管理方法，并精心规划部署。同时，WLAN使用分布式管理方法，其中部署在大多数情况下是无计划和混乱的。其次，5G在授权频段中运行，没有外部干扰，而WLAN在共享频段中运行，它们相互干扰，并与其他技术的设备相互干扰。ML 4 WIFI项目将解决这些问题。