# A Brief Review of Multipath TCP for Vehicular Networks
## 2 TCP and MPTCP
### Overview of Studies on TCP
Delay-based FAST TCP can satisfy the first two requirements, but it still causes TCP unfairness. In order to satisfy all the requirements, [PERSON] et al. [21] proposed an approach called CTCP (compound TCP). The CTCP adds a delay-based component to the standard TCP, and combines the delay signal and loss signal to adjust the congestion window. The consideration of RTT in the congestion control makes CTCP better handle the TCP unfairness in a lossless communication environment. The above-mentioned approaches generally adopt a pre-determined policy with fixed system parameters for implementing congestion control. They are incapable of satisfying the performance requirements under dynamic network environments. To make the congestion control protocol adapt to dynamic environments, [PERSON] et al. [22] integrate a reinforcement-based Q-learning framework with TCP, called QTCP. In QTCP, the congestion control gradually converges to the best rule without prior knowledge of the new environment. Hence, QTCP is capable of adjusting congestion control corresponding to a new network scenario. Moreover, they developed a generalization-based Kanerva coding function approximation approach to reduce the computation complexity and necessary state space, therefore reducing the training time. In most of the TCP variants, the congestion window starts from a small initial window (IW), specifically, 2, 4, or 10. For a short flow, TCP suffers from the slow start problem as the flow is still in additive increase state before the session ends. For a long flow service, the conventional manually- and statically-configured congestion control is not able to fulfill the latest network performance requirements. To address the above two challenges, [PERSON] et al. [23] proposed a TCP reinforcement learning (RL) technique, called TCP-RL. The TCP-RL dynamically adjusts the IW and congestion control to improve TCP flow transmission efficiency. Such learning based TCP variants are able to provide a higher throughput than rule-based TCP variants. The above-mentioned research efforts are summarized in Table 1. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Implementation** & **Type** & **Efficiency** & **RTT Fairness** & **TCP Fairness** & \begin{tabular}{c} **Dynamically** \\ **Adjusting** \\ \end{tabular} \\ \hline \multirow{3}{*}{Standard TCP} & \begin{tabular}{c} Implementing \\ conservative AIMD and \\ congestion control \\ \end{tabular} & Loss-based & Low & Yes & Yes & No \\ \hline \multirow{3}{*}{CUBIC} & \begin{tabular}{c} Modifying the \\ increase/decrease \\ parameters of standard \\ TCP more aggressively \\ \end{tabular} & Loss-based & High & No & Yes & No \\ \hline \multirow{3}{*}{STCP} & \begin{tabular}{c} Modifying the \\ increase/decrease \\ parameters of standard \\ TCP more aggressively \\ \end{tabular} & Loss-based & High & No & No & No \\ \hline \multirow{3}{*}{BIC-TCP} & \begin{tabular}{c} Modifying the \\ increase/decrease \\ parameters of standard \\ TCP more aggressively \\ \end{tabular} & Loss-based & High & No & No & No \\ \hline \multirow{3}{*}{Fast TCP} & \begin{tabular}{c} Adjusting the sending \\ rate based on the \\ RTT variation \\ \end{tabular} & Delay-based & High & Yes & No & No \\ \hline \multirow{3}{*}{BBR} & \
\begin{tabular}{c} Adjusting the sending \\ rate based on the RTT \\ variation and bandwidth \\ \end{tabular} &
\begin{tabular}{c} Congestion \\ -based \\ \end{tabular} & High & No & Yes & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of single path TCP studies.