# Ad-BBR: Enhancing Round-Trip Time Fairness and Transmission Stability in TCP-BBR
## 2 Related Work
[15] tackled RTT fairness issues in BBR under high-speed wireless network conditions, such as 5G and Wi-Fi. They developed a fluid model to analyze fairness differences and proposed real-time adjustments to ProbeBw and _pacing_gain_ to improve fairness, maintain high throughput, and minimize retransmissions. However, their approach lacks fine-grained analysis of the regulation process, leading to unnecessary sending rate reductions and bandwidth waste in situations where rate adjustments are not needed. This issue arises because the adaptive regulation mechanism does not account for varying queueing states, preventing the link from operating at maximum efficiency. [PERSON] et al. [23] proposed a BBR-based congestion control algorithm (S2B2C) along with a flow-path bounded priority scheduling mechanism (S2 PS) in 2024. Their approach enhances MPQUIC throughput and fairness in heterogeneous networks through shared bottleneck detection and dynamic rate adjustment and mitigates network instability by employing mixed-integer programming for scheduling. However, the study did not verify BBRv2 compatibility and was limited to static topologies, which restricts the analysis of dynamic network adaptability. [PERSON] et al. [24] introduced a reinforcement learning-based adaptive congestion control algorithm, ABBR, designed to optimize network throughput, delay, and fairness by dynamically selecting pacing gain sequences among various BBR variants. However, ABBR exhibits poor fairness when deployed alongside loss-based algorithms (e.g., Cubic) and does not adequately address scheduling optimization via mixed-integer programming. In 2025, [PERSON] et al. [25] proposed the BBR-R algorithm, which enhances RTT fairness and reduces queuing delays in multi-stream scenarios by dynamically adjusting delay detection intervals and optimizing packet counts during the probing phase. However, the experiments were conducted in idealized scenarios--excluding large-scale multi-stream and complex network topologies--and the parameter settings were based on empirical observations rather than adaptive mechanisms. Moreover, the algorithm's fairness when coexisting with other congestion control methods was not sufficiently validated. [PERSON] et al. [26] proposed the BBR-EBE+ algorithm, which enhances BBR's bandwidth estimation by accurately detecting and filtering excessively high ACK rates and delaying bandwidth update estimations. This approach effectively reduces average queuing delays and modestly increases throughput, yet it still faces notable challenges in terms of RTT fairness. Google has introduced the BBRv2 algorithm [27] to address the shortcomings of BBRv1. BBRv2 improves bandwidth estimation and adapts more accurately to the available link bandwidth while minimizing queue buildup. By incorporating packet loss rate and ECN (Explicit Congestion Notification) information, BBRv2 allows for quicker determination of when the StartUp phase ends, avoiding excessive queuing caused by over-probing. While BBRv2 offers better adaptability than BBRv1 [28; 29], it still faces issues with fairness, especially in packet loss scenarios, which leads to competitive unfairness [10]. In response, Google introduced BBRv3 in July 2023 [11] to address fairness convergence and post-congestion recovery speed. BBRv3 provides better fairness than its predecessors when operating alongside traditional packet loss-based congestion control algorithms. However, its performance in high-speed wireless networks with varying RTTs remains unsatisfactory. Despite ongoing updates from Google, experiments have revealed that the long-short-flow RTT fairness problem persists in BBR congestion control applications [30]. Therefore, continued research is necessary to enhance RTT fairness and ensure stable, smooth data transmission based on real-time link states, ultimately contributing to the final version of BBR