# PROFEE: A Probabilistic-Feedback Based Speed Rate Adaption for IEEE 802.11 bc
## 2 Related Work
Further, the authors use distributional reinforcement learning in [16] and evaluated their algorithm regarding the data rate of the broadcast messages and the reception success rate at STAs. In [17], the authors proposed MuDRA, a rate adaption algorithm for multicast flows. MuDRA relies on the information of the K STAs with the worst channel conditions, which the AP selects. Once selected, these STAs send their packet delivery ratio after a short random interval to avoid collisions. In order to reach the target rate, the proposed algorithm monitors the number of devices switching from PDR of 100% to 0% to satisfy the maximum permitted number of abnormal devices. Authors in [18] designed and tested AMuSe, a mechanism to broadcast packets to a large group of STAs. AMuSe relies on the cluster formation of adjacent neighbors and feedback from cluster heads. Although this might be a good technique in static scenarios, cluster formation might lead to long delays and lots of overhead in the presence of mobility. AMuSe provides rate adaption provided by MuDRA [17] to transmit at the highest possible bitrate while meeting some constraints. AMuSe is the only proposal verified on a testbed with over 150 STAs [19]. Further, the authors of [20] proposed ATRaCt, a cross-layer rate control for wireless multicast that takes information from the transport layer to modify the data rate of the link layer. The transport layer information is taken using the NORM protocol [3]. Also, ATRaCt periodically sends MSRq control messages using the base transmission rate to get statistics that are obtained via MSRp messages. Table 1 shows a summary of the above literature review, which denotes that most efforts related to feedback acquisition in broadcast services consider feedback from ACKs and/or NACKs in various ways. Other works use RTS/CTS messages to avoid collisions, and many seek the reception of every message from all STAs. Different from all these works, our proposal not only introduces a probability in the transmission of ACK/NACK messages that allows for the estimation of the number of STAs receiving messages correctly/incorrectly but also, from the percentage of both, collisions and absent periods (silence). The latter refers to the scenario in which the AP does not receive an ACK/NACK message in response to the transmission of a broadcast message. In other words, our pro  
posal can estimate the number of STAs receiving messages correctly/incorrectly from three different events simultaneously and independently of each other (i.e., successfully received ACK/NACK messages, ACK/NACK collisions, and ACK/NACK silences). The proposed method relies on traditional ACK/NACK transmissions. It requires no additional signaling or hardware to quickly and reliably estimate the percentage of STAs receiving messages correctly/incorrectly at the present MCS, which only takes a few seconds to converge to the actual number of STAs. On top of that, our method can use this estimation to select the best possible MCS while keeping the percentage of STAs that do not receive broadcast transmission under the desired levels.