The innovation of Wi-Fi 6, IEEE 802.11ax, was be approved as the next sixth-generation (6G) technology of wireless local area networks (WLANs) by improving the fundamental performance of latency, throughput, and so on. The main technical feature of orthogonal frequency division multiple access (OFDMA) supports multi-users to transmit respective data concurrently via the corresponding access points (APs). However, the conventional IEEE 802.11 protocol for Wi-Fi roaming selects the target AP only depending on received signal strength indication (RSSI) which is obtained by the received Response frame from the APs. In the long term, it may lead to congestion in a single channel under the scenarios of dense users further increasing the association delay and packet drop rate, even reducing the quality of service (QoS) of the overall system. In this paper, we propose a multi-agent deep Q-learning for fast roaming (MADAR) algorithm to effectively minimize the latency during the station roaming for Smart Warehouse in Wi-Fi 6 system. The MADAR algorithm considers not only RSSI but also channel state information (CSI), and through online neural network learning and weighting adjustments to maximize the reward of the action selected from Epsilon-Greedy. Compared to existing benchmark methods, the MADAR algorithm has been demonstrated for improved roaming latency by analyzing the simulation result and realistic dataset.

本研究提出了一种多代理深度Q学习算法(MADAR)，在Wi-Fi 6系统中实现智能仓库的快速漫游，通过考虑不仅仅是收到的信号强度指示（RSSI）还包括信道状态信息（CSI），通过在线神经网络学习和加权调整来最大化Epsilon-Greedy选择的行动的奖励，相对于现有基准方法，MADAR算法在改善漫游延迟方面表现更优。

IEEE 802.11ax Wi-Fi系统中分布式多智能体深度Q学习实现快速漫游