  In IEEE 802.11bc, the broadcast mode on wireless local area networks (WLANs),
data rate control that is based on acknowledgement (ACK) mechanism similar to
the one in the current IEEE 802.11 WLANs is not applicable because ACK
mechanism is not implemented. This paper addresses this challenge by proposing
ACK-less data rate adaptation methods by capturing non-broadcast uplink frames
of STAs. In IEEE 802.11bc, an use case is assumed, where a part of STAs in the
broadcast recipients is also associated with non-broadcast APs, and such STAs
periodically transmit uplink frames including ACK frames. The proposed method
is based on the idea that by overhearing such uplink frames, the broadcast AP
surveys channel conditions at partial STAs, thereby setting appropriate data
rates for the STAs. Furthermore, in order to avoid reception failures in a
large portion of STAs, this paper proposes deep reinforcement learning
(DRL)-based data rate adaptation framework that uses a sim-to-real approach.
Therein, information of reception success/failure at broadcast recipient STAs,
that could not be notified to the broadcast AP in real deployments, are made
available by simulations beforehand, thereby forming data rate adaptation
strategies. Numerical results show that utilizing overheard uplink frames of
recipients makes it feasible to manage data rates in ACK-less broadcast WLANs,
and using the sim-to-real DRL framework can decrease reception failures.
