ACK-Less Rate Adaptation for IEEE 802.11bc Enhanced Broadcast Services Using Sim-to-Real Deep Reinforcement Learning
T. Kanda, Y. Koda, K. Yamamoto, T. Nishio

TL;DR
This paper proposes an ACK-less data rate adaptation method for IEEE 802.11bc WLANs using overheard uplink frames and a sim-to-real deep reinforcement learning framework to improve broadcast reception success.
Contribution
It introduces a novel ACK-less rate adaptation approach leveraging overheard uplink frames and a sim-to-real DRL framework for efficient data rate management in broadcast WLANs.
Findings
Overhearing uplink frames helps in channel condition assessment.
The DRL-based approach reduces broadcast reception failures.
Sim-to-real training improves adaptation effectiveness.
Abstract
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…
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