# A Modified TCP BBR to Enable High Fairness in High-Speed Wireless Networks
## 2 Related Work  
Since the launch of BBR, several studies have identified certain problems in BBRv1 (BBR version 1). [PERSON] et al. [4] observed that there is a bandwidth discrepancy between elephant and mouse flows sharing the bottleneck link due to the higher BDP of the elephant flow. [PERSON] et al. [5] found that long queues are created during the startup phase, inhibiting existing traffic. [PERSON] et al. [6] found that in cases where there are multiple flows managed by BBRv1, overestimated transmission rates can lead to standing queues exceeding 1.5 times the BDP, resulting in packet loss and unfair bandwidth sharing between flows. To address these issues, Google introduced BBRv2 [7] in 2018, adding Explicit Congestion Notification (ECN) and packet loss rates. In their evaluation of various BBR versions, BBRv2 could not provide fair bandwidth sharing in the presence of other BBR flows with different RTTs and loss-based TCP flows [9; 10]. [PERSON] et al. [11] conducted an experimental evaluation comparing BBRv1 and BBRv2 and found that BBRv2 still consumes more bandwidth for long RTT flows in networks with larger buffers. [PERSON] et al. [12] pointed out BBRv2's improvements in terms of traffic fairness and improved coexistence with CUBIC and Reno. However, they also noted that BBRv2 trades performance for better fairness under losses. The BBRv3 [8] aims to fix the bugs in BBRv2 and optimize the performance parameters. Despite the updates and improvements in BBRv2 and BBRv3, experiments show that RTT unfairness still exists [13]. With regard to inter-protocol fairness, [PERSON] et al. [14] evaluated BBR's inter-protocol fairness when the traffic consists of flows of different sizes. [PERSON] et al. [15] proposed Modest BBR, which adjusts the rate according to the network situation, reduces retransmission, and realizes high throughput and inter-protocol fairness. [PERSON] et al. [16] provided the first model capturing BBR's behavior in competition with loss-based CCAs. [PERSON] et al. [17] proposed BBR convergence window scaling (BBR-CWS), which adjusts the size of CWND through packet loss feedback. It reduces excessive retransmission and finds a balance between algorithms. [PERSON] et al. [18] proposed a BBR with Adjusting RTprop (BAR), which adjusts RTprop so that BBR obtains fair bandwidth with CUBIC. To solve the RTT fairness problem, [PERSON] et al. [19] proposed the BBQ algorithm. By setting the upper limit of the detection time, long RTT flows are prevented from preempting the bandwidth of short RTT flows. When no persistent queue is detected, BBQ reverts to the original BBR mechanism to maintain bandwidth detection speed. [PERSON] et al. [20] proposedan Adaptive BBR algorithm, which alleviates the fairness between different RTT flows by changing the detection period and pacing gain of BBR. [PERSON] et al. (2018) proposed Delay-Aware BBR, which improves the fairness between different RTT flows. In our previous research, we also optimized RTT fairness by adjusting the sending rate ([PERSON] et al., 2018) to alleviate RTT unfairness. [PERSON] et.al. (2018) introduced BBR-With Enhanced Fairness (BBR-EPRA), dynamically adjusting Congestion Window (CWND) based on buffer queue status for equitable competition among different RTT flows. However, these algorithms rely on accurate real-time queue estimation, which is challenging in dynamic network conditions. BBRs have been applied to different network scenarios to improve service quality ([PERSON] et al., 2018; [PERSON] et al., 2018; [PERSON] et al., 2018). [PERSON] et.al. (2018) have developed an enhanced Stateful-TCP technique that converts BBRs into new S-BBRs to accelerate their startup performance and improve throughput in 5G networks. For BBRs in Wi-Fi and 5G networks, [PERSON] et al. (2018) proposed an improved BBR, called Yinker, which dynamically adjusts the pacing gain of BBR based on network conditions, including loss rate and congestion level. [PERSON] et.al.