In this paper, a reinforcement-learning-based scheduling framework is
proposed and implemented to optimize the application-layer quality-of-service
(QoS) of a practical wireless local area network (WLAN) suffering from unknown
interference. Particularly, application-layer tasks of file delivery and
delay-sensitive communication, e.g., screen projection, in a WLAN with enhanced
distributed channel access (EDCA) mechanism, are jointly scheduled by adjusting
the contention window sizes and application-layer throughput limitation, such
that their QoS, including the throughput of file delivery and the round trip
time of the delay-sensitive communication, can be optimized. Due to the unknown
interference and vendor-dependent implementation of the network interface card,
the relation between the scheduling policy and the system QoS is unknown.
Hence, a reinforcement learning method is proposed, in which a novel Q-network
is trained to map from the historical scheduling parameters and QoS
observations to the current scheduling action. It is demonstrated on a testbed
that the proposed framework can achieve a significantly better QoS than the
conventional EDCA mechanism.

提出并实现了一种基于强化学习的调度框架，用于优化受到未知干扰影响的实际无线局域网（WLAN）的应用层服务质量（QoS）。通过调整争用窗口大小和应用层吞吐量限制，联合调度具有文件传递和对延迟敏感通信的应用层任务，如屏幕投射，以优化它们的 QoS，包括文件传递的吞吐量和对延迟敏感通信的往返时间。

ReinWiFi: WiFi 网络应用层 QoS 优化的基于强化学习的框架

ReinWiFi: A Reinforcement-Learning-Based Framework for the  Application-Layer QoS Optimization of WiFi Networks

Wireless Local Area Network (WLAN) has become a promising choice for indoor
positioning as the only existing and established infrastructure, to localize
the mobile and stationary users indoors. However, since WLAN has been initially
designed for wireless networking and not positioning, the localization task
based on WLAN signals has several challenges. Amongst the WLAN positioning
methods, WLAN fingerprinting localization has recently achieved great attention
due to its promising results. WLAN fingerprinting faces several challenges and
hence, in this paper, our goal is to overview these challenges and the
state-of-the-art solutions. This paper consists of three main parts: 1)
Conventional localization schemes; 2) State-of-the-art approaches; 3) Practical
deployment challenges. Since all the proposed methods in WLAN literature have
been conducted and tested in different settings, the reported results are not
equally comparable. So, we compare some of the main localization schemes in a
single real environment and assess their localization accuracy, positioning
error statistics, and complexity. Our results depict illustrative evaluation of
WLAN localization systems and guide to future improvement opportunities.

本文综述了 WLAN 指纹定位方法在实际部署中遇到的挑战以及最新解决方案，并在单个真实环境中比较了不同定位方案的准确性和复杂性。