# Ad-BBR: Enhancing Round-Trip Time Fairness and Transmission Stability in TCP-BBR
## 1 Introduction  
With the rapid growth of Internet applications and the continuous evolution of wireless network technologies, such as IoT, 5G, and Wi-Fi, global data traffic is experiencing an explosive increase. Simultaneously, the network environment is becoming increasingly complex and dynamic, with factors like diverse user behaviors, constant updates to terminal devices, and frequent link condition fluctuations presenting unprecedented challenges to transport layer protocols and congestion control algorithms. Traditional congestion control algorithms based on packet loss (e.g., Reno and Cubic) often struggle to balance high throughput and low latency in high-bandwidth, low-latency, and highly dynamic scenarios while also exhibiting fairness issues in multi-stream competitive environments [1, 2]. To address these bottlenecks, Google introduced the BBR algorithm in 2016 [3], which models link transmission by actively probing the bottleneck bandwidth (BtlBw) and the minimum round-trip propagation time (RTprop). BBR defines the maximum effective transmission rate of a link as the product of the BtlBw and RTprop (i.e., the bandwidth-delay product, BDP). By estimating these parameters in real time, BBR dynamically adjusts the sending rate to approximate the BDP, maximizing throughput while minimizing transmission latency. The BBR algorithm has been deployed across various Google platforms, such as B4 WAN and YouTube, and integrated into the Linux kernel. In real high-speed wireless network environments (e.g., 5G and Wi-Fi), applications often involve both bandwidth-demanding long RTT streams and delay-sensitive short RTT streams, resulting in increasingly noticeable bandwidth competition between the two types of streams [4; 5]. Due to their distinct characteristics and diverse causes, these streams have different QoS (Quality of Service) requirements and round-trip times (RTTs), posing significant challenges for the BBR algorithm. Specifically, to account for potential Acknowledge character (ACK) delays and queue buildup in real networks, BBR's control logic adjusts the congestion window (CWND) to be slightly larger than the calculated ideal value, preventing transmission stalls [6]. While this strategy helps avoid link idling, it also allows excess data to be injected into the network, leading to the phenomenon of data over-injection [7]. This not only causes queue buildup and increases the RTT but also heightens the risk of packet loss, ultimately affecting overall transmission performance and network stability. In terms of fairness, when multiple BBR flows share the same network bottleneck link, flows with different round-trip times (RTTs) are unable to allocate bandwidth fairly, as long RTT flows tend to obtain excessive bandwidth, while short RTT flows struggle to achieve reasonable transmission rates. Bandwidth allocation is, thus, heavily influenced by the RTTs of competing flows [7; 8]. This phenomenon further limits the applicability of the BBR algorithm in multi-flow competitive environments [6]. Additionally, the BBR algorithm periodically enters the ProbeRT phase without assessing its necessity, aiming to determine the minimum RTT with emptied queues. This results in bandwidth wastage and transmission interruptions, leading to unstable data transmission and undermining the original goals of the BBR algorithm [9]. To address these issues, the focus of current research is on effectively preventing data over-injection and reducing queue delay while preserving the high-throughput and low-latency advantages of BBR. Additionally, improving fairness in multi-stream competition to achieve smoother transmission has been a key challenge. However, most studies have concentrated on enhancing RTT fairness while largely overlooking throughput degradation and failing to thoroughly investigate the redundant triggering behavior of the ProbeRTT phase.