# PROFEE: A Probabilistic-Feedback Based Speed Rate Adaption for IEEE 802.11 bc
## 6 Discussion  
When evaluating the performance of the three estimators for Algorithm 1, the initial expectation was that all three would yield similar estimations, thereby enhancing the system's robustness. However, it became evident that the silence estimator outperformed the others for a finite number of messages, providing superior results. The explanation for this outcome is the frequent occurrence of packet capture during the experiments. Packet capture enables a receiver to retrieve a single packet (typically the one with the highest signal-to-noise ratio (SNR) from a collision of packets. This mechanism transforms a collision event into a successful reception event, introducing discrepancies or errors in the collision and correct feedback estimators. Interestingly, the silence estimator remained unaffected by this issue and consistently delivered accurate estimations of the number of STAs receiving messages, demonstrating remarkable accuracy (within a 1% margin of the actual number of STAs). In the evaluation of Algorithm 2 in Section 5.3, focusing on the adjustment of MCS, the experiments were conducted using a high value for MCS(i). However, alternative strategies, such as commencing with a middle-valued MCS(i), can be explored. Furthermore, instead of the incremental or decremental adjustment of the current MCS(i) by one step per frame, Algorithm 2 could evaluate the percentage of STAs experiencing message reception errors to execute larger jumps across multiple MCS indexes after each frame. Irrespective of the initial MCS index chosen, the results illustrate that the ultimate MCS outcome remains consistent--solely the time taken to achieve convergence varies. Yet, commencing with a higher MCS offers an advantage by expediting the transmission of broadcast messages. This facilitates a quicker estimation by the AP of the number of STAs that receive messages inaccurately, in contrast to initiating the process with a lower MCS. Due to limitations in space, we only presented the results of experiments using a NACK range set between 10% and 20%. Nevertheless, experiments were conducted with varying NACK ranges, revealing that a reduction in the NACK range (e.g., 5% to 10%) led to the AP selecting a lower MCS index. Conversely, a higher NACK range (e.g., 20% to 40%) resulted in a higher MCS index compared to the findings detailed in Section 5.3. While the present analysis exclusively focuses on static scenarios, it is worth noting that, even in scenarios where STAs are mobile, the time it takes for an STA to traverse a single disk associated with a specific MCS(i) (under the scenario depicted in Figure 5) ranges between 15 to 50 s. Considering that the proposed method achieves convergence to the optimal MCS within 1 to 8 s, the convergence will most likely occur before an STA transitions to a different disk area due to movement. It's also essential to emphasize that the 802.11 bc standard mainly envisions static scenarios. This assumption aligns with the understanding that engaging in activities like viewing multimedia content while walking should be discouraged. Figure 10: Rate Adaption for 1000 STAs. Nonetheless, the proposed method presents its limitations and tradeoffs. On one hand, the time it takes to converge is directly proportional to the rate of broadcast messages. In some high-quality video stream applications, it will take the method a very short time to converge (less than one second); however, if the content is being sent at a slow pace, convergence times to find the optimum MCS will take much longer. Also, this work assumes that only one AP operates in the venue. Having one or more APs interfering with the AP sending broadcast content might cause a collision of broadcast and feedback messages, adding errors in the estimation process. In the future, we plan on modifying the base method to make it resilient to this constraint. Finally, we evaluated the performance of the method in a simulation environment.