WiFi-FRL Dataset (v1.0)

This dataset contains simulation outputs for the WiFi-FRL study evaluating Fixed, Random, Reinforcement Learning (RL), and Federated Reinforcement Learning (FRL) multi-link (and multi-channel) activation policies in dense multi-AP Wi-Fi networks. Each subfolder includes accumulated (acc*) and temporal/CDF (cdf*) rate matrices across network densities (APs ∈ {2,4,8,12,16}), multiple realizations (MAX_SIM), and iterations (MAX_ITER), matching the experiment configuration in the associated Python simulator availabe at the Github repository (https://github.com/wirelessATwest/WiFi-FRL). See README for file naming and reproduction steps.本数据集包含WiFi-FRL研究的仿真输出结果，该研究针对高密度多接入点Wi-Fi网络中的固定、随机、强化学习（Reinforcement Learning，RL）以及联邦强化学习（Federated Reinforcement Learning，FRL）多链路（及多信道）激活策略展开评估。每个子文件夹均涵盖不同网络密度（接入点数量AP∈{2,4,8,12,16}）、多次仿真实现（MAX_SIM）与迭代次数（MAX_ITER）下的累积（acc*）与时域/累积分布函数（CDF，cdf*）速率矩阵，与该研究配套的Python仿真器的实验配置一致，该仿真器可从GitHub仓库（https://github.com/wirelessATwest/WiFi-FRL）获取。有关文件命名规则与实验复现步骤，请参阅README文件。