  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.
