# On the Fairness of Internet Congestion Control over WiFi with Deep Reinforcement Learning
## 6 Conclusions and Future Work  
This paper presents an approach to improving the fairness of TCP CCA in WiFi networks using DRL. To address unfairness and potential starvation issues in traditional CCAs, we developed a dynamic CCA switching mechanism that uses offline DRL to select the most appropriate CCA based on real-time network conditions. Our analysis and experiments indicate that this approach can help reduce congestion and improve fairness among competing network flows. Key aspects of our work include delay variation analysis, the development of a CCA switching mechanism, and validation in a testbed environment. The results show improvements in throughput stability, reduced RTT and jitter, and lower packet loss, contributing to better overall network performance. Considering both QoS and QoE metrics, our approach aims to balance technical performance with user experience.  
\begin{table}
\begin{tabular}{c l c c c c c} \hline \hline \multirow{2}{*}{**Our Setup**} & \multirow{2}{*}{**CCAs over WiFi Network Settings**} & \multirow{2}{*}{**2 Clients (C1, C2)**} & \multicolumn{4}{c}{**Aggregate Performance Metrics**} \\ \cline{3-6}  & & & **Throughput** & **RTT** & **Jitter** & **Loss** \\ \hline \multirow{2}{*}{Seen.} & CUBIC vs. BBR & CUBIC(C1) & 79.44 Mbps & 21.44 ms & 0.75 ms & 12.8\% \\  & (before) & BBR(C2) & 4.08 Mbps & 2.32 ms & 23.99 ms & 19.35\% \\ \cline{2-6} \multirow{2}{*}{1} & CUBIC vs. BBR-\(>\)CUBIC & CUBIC(C1) & 40.64 Mbps & 16.30 ms & 1.09 ms & 21.10\% \\  & (after) & CUBIC(C2) & 38.72 Mbps & 13.01 ms & 0.82 ms & 11.45\% \\ \hline \multirow{2}{*}{Seen.} & PCC vs. BBR & PCC(C1) & 77.36 Mbps & 6.27 ms & 0.84 ms & 48.97\% \\  & (before) & BBR(C2) & 12.32 Mbps & 22.02 ms & 1.163 ms & 11.83\% \\ \cline{2-6} \multirow{2}{*}{2} & PCC vs. BBR-\(>\)PCC & PCC(C1) & 44 Mbps & 15.08 ms & 1.722 ms & 9.22\% \\  & (after) & PCC(C2) & 40.4 Mbps & 15.98 ms & 1.53 ms & 7.21\% \\ \hline \multirow{2}{*}{Seen.} & BBR vs. PCC & BBR (C1) & 20.48 Mbps & 13.40 ms & 2.85 ms & 29.44\% \\  & (before) & PCC(C2) & 42.08 Mbps & 15.76 ms & 2.14 ms & 4.17 \% \\ \cline{2-6} \multirow{2}{*}{3} & BBR vs. PCC-\(>\)BBR & BBR(C1) & 42.56 Mbps & 14.63 ms & 1.16 ms & 9.35\% \\  & (after) & BBR (C2) & 45.6 Mbps & 15.78 ms & 1.53 ms & 7.20\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of results from network emulation experiments.  
The study highlights the potential of DRL for adaptive network management, with offline DRL offering a practical implementation path without the complexities of online learning.  
Our work has certain limitations. The model was tested in offline environments with precollected WiFi data, and its real-time performance in hybrid or wired networks has not been explored. Furthermore, the study focused on common TCP variants such as Cubic, PCC, BBR, and Copa, which may not extend to machine-learning-based TCP algorithms such as Remy, Aurora, and Astraea. Future research should address these limitations by testing the model in real-time network environments, integrating active queue management, developing CCAs for noncongestive delay jitter, and using data from a broader range of machine learning-based TCP algorithms to improve generalization.  
Conceptualization, J.K.; Methodology, S.K.S., S.R.P. and J.K.; Software, S.K.S., S.R.P. and J.K.; Formal analysis, S.R.P. and J.K.; Investigation, S.K.S., S.R.P. and J.K.; Writing--review & editing, S.K.S. and S.R.P.; Visualization, S.K.S.; Supervision, S.R.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.