## 5 Experiment
### Evaluation  
The airtime fairness algorithm proposed in this paper is a QoS scheduling algorithm designed specifically for Wi-Fi. Unlike general Wi-Fi queue scheduling algorithms, QoS scheduling and queue scheduling serve distinct purposes but work together to enhance the performance and user experience of wireless networks. The primary goal of a Wi-Fi QoS scheduling algorithm is to manage and optimize the transmission of various types of traffic within the wireless network to meet the specific quality of service requirements of different applications. A common Wi-Fi QoS mechanism is Enhanced Distributed Channel Access (EDCA), defined in the IEEE 802.11 standard. EDCA classifies traffic into four types: voice, video, background, and best effort. Each has different priorities and parameters to ensure better service for high-priority traffic. In contrast, Wi-Fi queue scheduling algorithms manage packet queues on access points or terminal devices, handling enqueue and dequeue operations, transmission resource allocation, and traffic control. Examples of common queue scheduling algorithms include Drop-tail [24], RED [25], and CoDel [26], which determine the order and timing of packet transmission. In this study, the classic Drop-tail queue was utilized. Figure 10: Airtime statistics method in ns-3. **Download Scenario. ** We begin by comparing the performance of three QoS algorithms in a download scenario: the default QoS algorithm, general airtime fairness, and the airtime fairness algorithm proposed in this paper. Throughput results are tested under varying TCP ACK delay conditions, as shown in Table 2.  
The experimental results reveal that the default QoS algorithm in this scenario aligns with throughput fairness, as the throughput for the three stations is nearly identical. In contrast, general airtime fairness tends to favor stations with higher wireless rates, providing them with more transmission opportunities. This approach results in higher effective throughput for these stations, thereby increasing the overall throughput of the wireless link. Our proposed airtime fairness algorithm, however, produces more balanced throughput among the stations by adjusting airtime calculations differently for each station. As a result, while the total throughput achieved by our algorithm exceeds that of the default QoS algorithm, it falls short of the throughput provided by general airtime fairness. We would like to further explain that better airtime fairness does not equate to higher total throughput. For instance, if all wireless channel resources are allocated to the fastest STA, the highest total throughput would be achieved. However, this is clearly not a reasonable approach. Consistent results across experiments with different TCP delayed ACK parameters demonstrate that our QoS algorithm effectively adjusts the stations' access opportunities to the wireless channel based on their airtime, thereby achieving airtime fairness. The specific airtime for each STA in this experiment is detailed in Table 3.  
The experimental results indicate that the airtime fairness index of the default QoS algorithm is quite high, even significantly surpassing that of the general airtime fairness algorithm. Moreover, the default QoS algorithm can be viewed as throughput fairness in this test scenario. This discrepancy arises because the general airtime fairness algorithm miscalculates the airtime for each STA, leading to a substantial deviation from the actual results. In contrast, our proposed airtime fairness algorithm accurately calculates airtime, enabling the AP to achieve excellent airtime fairness through a QoS-based scheduling approach. The experimental results closely align with the theoretical optimal solution, which has a fairness index of 1. **TCP Congestion Control. ** Since the primary goal of this paper is to improve the TCP airtime fairness, it is essential to evaluate the performance of our QoS-based scheduling method under different congestion control algorithms.