# On the Fairness of Internet Congestion Control over WiFi with Deep Reinforcement Learning
## 2 Background and Related Works
### TCP Fairness and ML Approaches  
Inter-flow fairness, ensuring equitable distribution of network resources among competing flows, is a fundamental aspect of congestion control. However, achieving fairness poses challenges, as highlighted by recent studies. These studies identify scenarios where starvation can occur despite efforts to maintain fairness, particularly in delay-bounding congestion control algorithms. Recently proposed solutions, including reinforcement learning-based approaches that aim to optimize host CCAs and bottleneck Active Queue Management (AQM) schemes [26], such as leveraging Deep Deterministic Policy Gradient (DDPG) [27] and Asynchronous Advantage Actor Critic (A3C) [28], aim to simultaneously improve throughput and ensure fairness [19].  
Network delay variations significantly impact congestion control algorithm performance and fairness among flows. Factors contributing to delay variations challenge traditional throughput and fairness models. Comparative studies of TCP congestion control mechanisms emphasize the benefits of loss-based algorithms for latency-sensitive flows and the fairness issues of delay-based algorithms [29; 30]. Empirical evidence highlights the potential for unfairness in delay-bounding congestion control algorithms, particularly when non-congestive network delay variations exceed certain thresholds. Improved fairness and performance have been achieved through adaptive and stable delay control algorithms. These advancements aim to address issues such as bufferbloat [31], which can lead to unfairness in network performance [32].  
Metrics for fairness evaluation play a crucial role in assessing network protocols. Throughput fairness, delay fairness, jitter fairness, and loss fairness are key metrics used to evaluate the distribution of network resources among flows. These metrics help identify potential issues and guide the development of fairer congestion control algorithms [3].  
ML and DRL frameworks for TCP offer promising avenues for improving congestion control algorithm fairness and performance. These frameworks enable the development of models that optimize network resource allocation while considering various QoE metrics. Integrating ML/DRL into congestion control algorithm selection processes can lead to more efficient and equitable network experiences [33; 34; 33].  
Traditional rule-based approaches, which employ predefined rules and logical conditions, often struggle under varying network conditions due to their lack of flexibility. Heuristic methods, while relying on rule-of-thumb strategies and practical experience, can also fall short in complex and dynamic environments because they lack the ability to adapt to real-time changes [35]. Thus, instead of directly comparing DRL methods against these more rigid approaches, we chose to focus on a DRL-based dynamic switching mechanism. This approach leverages DRL's ability to continually learn and adapt to varying network conditions, offering a significant advantage over static evaluation functions or less flexible methods.