# On the Fairness of Internet Congestion Control over WiFi with Deep Reinforcement Learning
###### Abstract  
For over forty years, TCP has been the main protocol for transporting data on the Internet. To improve congestion control algorithms (CCAs), delay bounding algorithms such as Vegas, FAST, BBR, PCC, and Copa have been developed. However, despite being designed to ensure fairness between data flows, these CCAs can still lead to unfairness and, in some cases, even cause data flow starvation in WiFi networks under certain conditions. We propose a new CCA switching solution that works with existing TCP and WiFi standards. This solution is offline and uses Deep Reinforcement Learning (DRL) trained on features such as noncongestive delay variations to predict and prevent extreme unfairness and starvation. Our DRL-driven approach allows for dynamic and efficient CCA switching. We have tested our design preliminarily in realistic datasets, ensuring that they support both fairness and efficiency over WiFi networks, which requires further investigation and extensive evaluation before online deployment.  
TCP unfairness; starvation; WiFi; dynamic CCA switching; congestion control algorithms (CCAs); Deep Reinforcement Learning +
Footnote †: journal: Computer Science  
0000-0001-9993-3000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-00000-00000-0000-0000-00000-0000-00000-000  
are reactive by nature, responding to congestion events after they have occurred, which can lead to network inefficiencies and degraded user experience.  
**Limitations of Delay-Based CCAs**: Delay-based CCAs, including Sprout [9], Remy [10], Vegas [11], FAST [12], BBR [13], PCC [14; 15], Copa [16], and Verus [17], have been developed to address the limitations of loss-based algorithms by adjusting CWND based on delay measurements rather than packet losses. While promising, these algorithms are prone to performance degradation and fairness issues, particularly in networks where non-congestive delay variations--such as those caused by ACK aggregation, thread variations, or end-host scheduling--can significantly affect their operation. These variations can lead to situations where some flows receive disproportionately low bandwidth, a condition known as \"starvation\", which severely impacts QoE [18].  
**Fairness and Starvation Issues**: Although delay-based CCAs are designed to improve network performance, they can inadvertently cause unfairness among competing flows, particularly under heterogeneous network conditions. For instance, if non-congestive delay variations exceed twice the equilibrium delay range, delay-based CCAs may become unstable, leading to starvation for certain flows. This issue is particularly problematic in WiFi networks, where varying signal strengths and interference can exacerbate these effects [19; 20; 21]. Existing studies often fail to adequately address these fairness issues, focusing instead on optimizing performance metrics like throughput or latency without considering the broader impact on user experience.  
**The Need for Dynamic Adaptation**: Current CCAs are typically designed with a fixed logic that does not dynamically adapt to changing network conditions [22]. This rigidity is a significant shortcoming in modern networks, where conditions can vary rapidly, particularly in wireless and mobile environments [23]. The inability of these algorithms to adapt to real-time changes in network conditions leads to suboptimal performance [24] and exacerbates the fairness and efficiency issues described above. Moreover, the lack of dynamic adaptation makes it difficult for CCAs to maintain optimal performance across diverse environments, from stable wired connections to fluctuating wireless networks.