# A Modified TCP BBR to Enable High Fairness in High-Speed Wireless Networks
## 1 Introduction  
In 2016, Google [1] proposed a new congestion control algorithm (CCA) based on Bottleneck Bandwidth and Round-Trip propagation time (BBR). BBR creates a network path model by measuring the available bottleneck bandwidth (BtlBw) and the Round-Trip propagation time (RTprop) to maximize delivery rate and minimize latency. Its improved performance and adaptability have led to widespread acclaim and interest since its launch. In high-speed wireless networks such as 5G or WiFi, there are a variety of applications that include both bandwidth-demanding long flows (e.g., video surveillance drone applications and Augmented Reality (AR)/Virtual Reality (VR)) and latency-sensitive short flows [2, 3]. In 5G network environments, the role of high-bandwidth long Round-Trip Time (RTT) flows and frequent short RTT flows are more pronounced, and they have different Quality of Service (QoS) and RTT requirements.  
Unfortunately, BBR cannot fairly share bandwidth with traffic with different RTTs. The first version of BBR (BBRv1) overestimated the sending rate by constantly filling the Bandwidth Delay Product (BDP), leading to problems such as queue stacking and RTT unfairness [4, 5, 6]. To overcome these limitations, BBRv2 (BBR version 2) [7] introduced mechanisms to better estimate and adapt to the actual available bandwidth and to minimize queue stacking. Although BBRv2 adapts better to network changes than BBRv1, RTT unfairness remains when competing with other flows. Google has recently released BBRv3(BBR version 3) [8], and while BBRv3 exhibits greater fairness to loss-based CCAs, it falls short in Next-Generation Networks (NGNs) with widely varying RTT requirements.  
In this paper, we mitigate the observed fairness challenges of BBR in high-speed but lossy networks, such as WiFi and 5G networks. BBR has been deployed in some application scenarios, but more research is needed to further improve BBR to ensure that there are no more potential vulnerabilities. As a result of this motivation, this paper makes the following contributions:  
1. The pacing rate control model is established, and the origin of BBR fairness issues is revealed. Because the RTprop and queuing delay affect the original pacing gain, the RTT fairness and inter-protocol fairness of BBR result.
2. The optimization algorithm BBR-Pacing Gain (BBR-PG), which adaptively adjusts the original fixed pacing gain to balance the sending rates of different flows in the bottleneck queue, is proposed. The phase time of the original BBR is optimized to accelerate the flow convergence.
3. BBR-PG is compared and analyzed by simulation and using a real network. The results show that BBR-PG maintains the throughput advantage of the original BBR, improves RTT fairness and inter-protocol fairness, and reduces retransmission.  
The rest of this article is arranged as follows: Section 2 introduces the principle of BBR and related optimization research. The theoretical model and derivation of the proposed algorithm are in Section 3. Section 4 is experimental results and analysis. Conclusions are discussed in Section 5.