论文标题
Inspire：分布式贝叶斯优化，以改善密集的wlans空间重复使用
INSPIRE: Distributed Bayesian Optimization for ImproviNg SPatIal REuse in Dense WLANs
论文作者
论文摘要
WLAN已超过有线网络，成为将设备连接到Internet的主要手段，由于无线电频谱中的空间稀缺，很容易解决性能问题。作为响应，IEEE 802.11AX和后续修订旨在通过允许在无线传输中的两个关键参数的动态更新：传输功率（TX_POWER）和灵敏度阈值（obss_pd）来增加无线通道的空间重复使用。在本文中，我们提出了Inspire，这是一种分布式解决方案，该解决方案基于高斯流程，以改善WLAN的空间重复使用。 Inspire对WLAN的拓扑并有利于访问点的利他行为的明确假设，从而使他们找到了其TX_POPTOR的足够配置，并且obs_pd参数具有WLAN的“更大商品”。我们证明了使用NS-3模拟器与其他最先进策略相比，激发灵感的优势，以及受密集的WLAN现实部署启发的两个示例。我们的结果表明，在短短几秒钟内，Inspire能够通过改善其公平性和吞吐量来大大提高操作WLAN的服务质量。
WLANs, which have overtaken wired networks to become the primary means of connecting devices to the Internet, are prone to performance issues due to the scarcity of space in the radio spectrum. As a response, IEEE 802.11ax and subsequent amendments aim at increasing the spatial reuse of a radio channel by allowing the dynamic update of two key parameters in wireless transmission: the transmission power (TX_POWER) and the sensitivity threshold (OBSS_PD). In this paper, we present INSPIRE, a distributed solution performing local Bayesian optimizations based on Gaussian processes to improve the spatial reuse in WLANs. INSPIRE makes no explicit assumptions about the topology of WLANs and favors altruistic behaviors of the access points, leading them to find adequate configurations of their TX_POWER and OBSS_PD parameters for the "greater good" of the WLANs. We demonstrate the superiority of INSPIRE over other state-of-the-art strategies using the ns-3 simulator and two examples inspired by real-life deployments of dense WLANs. Our results show that, in only a few seconds, INSPIRE is able to drastically increase the quality of service of operational WLANs by improving their fairness and throughput.
