Software-Defined Networking (SDN) is gaining a lot of traction in wireless systems with several practical implementations and numerous proposals being made. Despite instigating a shift from monolithic network architectures towards more modulated operations, automated network management requires the ability to extract, utilise and improve knowledge over time. Beyond simply scrutinizing data, Machine Learning (ML) is evolving from a simple tool applied in networking to an active component in what is known as Knowledge-Defined Networking (KDN). This work discusses the inclusion of ML techniques in the specific case of Software-Defined Wireless Local Area Networks (SD-WLANs), paying particular attention to the frame length optimization problem. With this in mind, we propose an adaptive ML-based approach for frame size selection on a per-user basis by taking into account both specific channel conditions and global performance indicators. By relying on standard frame aggregation mechanisms, the model can be seamlessly embedded into any Enterprise SD-WLAN by obtaining the data needed from the control plane, and then returning the output back to this in order to efficiently adapt the frame size to the needs of each user. Our approach has been gauged by analysing a multitude of scenarios, with the results showing an average improvement of 18.36% in goodput over standard aggregation mechanisms.

中文翻译：

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企业 SD-WLAN 中基于机器学习的自适应帧长优化

软件定义网络 (SDN) 在无线系统中获得了很大的吸引力，有几个实际的实现和提出了许多建议。尽管推动了从单一网络架构向更多调制操作的转变，但自动化网络管理需要能够随着时间的推移提取、利用和改进知识。除了简单地检查数据之外，机器学习 (ML) 正在从应用在网络中的简单工具演变为所谓的知识定义网络 (KDN) 中的主动组件。这项工作讨论了在软件定义无线局域网 (SD-WLAN) 的特定情况下包含 ML 技术，特别注意帧长度优化问题。考虑到这一点，我们通过考虑特定的信道条件和全局性能指标，提出了一种基于 ML 的自适应方法，用于基于每个用户的帧大小选择。依靠标准的帧聚合机制，该模型可以通过从控制平面获取所需的数据，然后将输出返回给该控制平面，从而无缝地嵌入到任何企业 SD-WLAN 中，以便有效地调整帧大小以适应需求。每个用户。我们的方法是通过分析多种场景来衡量的，结果显示，与标准聚合机制相比，吞吐量平均提高了 18.36%。通过从控制平面获取所需的数据，然后将输出返回给该控制平面，该模型可以无缝嵌入到任何企业 SD-WLAN 中，以便有效地调整帧大小以满足每个用户的需求。我们的方法是通过分析多种场景来衡量的，结果显示，与标准聚合机制相比，吞吐量平均提高了 18.36%。通过从控制平面获取所需的数据，然后将输出返回给该控制平面，该模型可以无缝嵌入到任何企业 SD-WLAN 中，以便有效地调整帧大小以满足每个用户的需求。我们的方法是通过分析多种场景来衡量的，结果显示，与标准聚合机制相比，吞吐量平均提高了 18.36%。