IEEE 802.11 WLAN网络中基于ml优化波束的无线电覆盖处理

Mehdi Guessous, L. Zenkouar
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引用次数: 2

摘要

动态无线电资源管理(RRM)是WLAN网络中无线局域网控制器(WLC)功能的主要组成部分。在密集和频繁变化的wlan中,它最大限度地提高了无线设备(WD)的传输机会,并保证符合设计的服务水平协议(SLA)。为了实现这种性能,WLC处理并应用基于接入点(AP)和上层应用服务的数据的全网优化无线电计划。这种覆盖处理需要无线电环境的“现实”建模方法和对频繁变化的快速适应。在本文中,我们建立了基于波束的无线电覆盖建模方法。我们提出了一种新的基于机器学习回归(MLR)的优化方法,并将其与基于nurbs的解决方案性能进行比较,作为替代方案。我们展示了这两种解决方案的处理时间非常相似。然而,我们基于mlr的解决方案比其替代方案具有更显著的预测精度增强。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ML-Optimized Beam-based Radio Coverage Processing in IEEE 802.11 WLAN Networks
Dynamic Radio Resource Management (RRM) is a major building block of Wireless LAN Controllers (WLC) function in WLAN networks. In a dense and frequently changing WLANs, it maximizes Wireless Devices (WD) opportunity to transmit and guarantees conformance to the design Service Level Agreement (SLA). To achieve this performance, a WLC processes and applies a network-wide optimized radio plan based on data from access points (AP) and upper-layer application services. This coverage processing requires a "realistic" modelization approach of the radio environment and a quick adaptation to frequent changes. In this paper, we build on our Beam-based approach to radio coverage modelization. We propose a new Machine Learning Regression (MLR)-based optimization and compare it to our NURBS-based solution performance, as an alternative. We show that both solutions have very comparable processing times. Nevertheless, our MLR-based solution represents a more significant prediction accuracy enhancement than its alternative.
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