# A Brief Review of Multipath TCP for Vehicular Networks
## 3 Congestion Control and Scheduler
### Congestion Control
#### 3.1.3 Lesson Learned  
In this subsection, we present some multipath congestion control mechanisms. Each mechanism has its own characteristic and is designed for a specific purpose. For example, LIA is proposed for solving the TCP-friendliness problem. LIA is supposed to solve the problem that a simple extension of TCP to multipath by applying AIMD on each path independently would result in TCP unfairness. As LIA and wVegas are extensions of the single-path TCP congestion control algorithm, they inherit some characteristics of single-path TCP. When the network condition is stable, a simple extension of single-path TCP to multiple paths can be an acceptable way. However, these methods have limitations in irregular network conditions such as lossy networks or heterogeneous networks. In order to overcome these limitations, some algorithms have been proposed. For example, DBCCA is designed for lossy heterogeneous network environments. Although these approaches show their advantages under certain network conditions, more extensive experiments are still needed to verify the effectiveness. On the other hand, new architectural design that combined with other techniques such as SDN should be considered to satisfy the goals. Due to the constant development of new technologies such as collaborative autonomous driving or remote surgery, the expectations for network performance are also rising. Especially, for vehicular networks in which the network topology and network condition are constantly changing due to the movement of vehicles. In this case, the single \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Main Metrics or Key Idea** & **Publication** & **Research Summary** & **Limitation/Advantages** \\ \hline Delay and loss signal & [PERSON] et al. [54] & A comparison study of different congestion control variants in three different scenarios. & A comparison study \\ \hline Rate distribution vector and RT & [PERSON] et al. [55] & A proposal that considers the delay difference of different paths in the congestion control. & Minimize the delay difference of paths/Possibility of sacrifice throughput \\ \hline Goodput & [PERSON] et al. [56] & An algorithm that consider in-order packets that sent to the application layer as the main metric. & Improving goodput/Possibility of sacrificing slow path throughput \\ \hline Experience driven & [PERSON] et al. [57] & A DRL-based congestion control framework that consists of LSTM-based accretion network. & Dynamically perform congestion control/Need time to train \\ \hline Adaptive & [PERSON] et al. [58] & A model free adaptive congestion control framework based on a fuzzy normalized neural network. & Adaptively adjusting congestion window/Take time to train \\ \hline Experience driven & [PERSON] et al. [59] & A proposal that utilizes the congestion rules learnt from observing the environments to adjust the congestion window size of subflows. & Adaptively adjusting window size/TCP unfriendliness \\ \hline Learning-based & [PERSON] et al. [60] & A proposal that employs deep deterministic policy gradient for learning the optimal congestion control strategies by interacting with the underlying network environment in satellite communications. & Optimizing congestion control strategy/No real system evaluation \\ \hline Adaptive & [PERSON] et al. [61] & A protocol for NDN, which first predicts the congestion level, and then adjusts the sending rate based on congestion level. & Adaptively adjusting congestion control/Not compatible with IP-based network \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._path TCP extensions and the existing algorithms designed for other purposes cannot meet the performance requirements. To overcome the limitation, some learning-based congestion control methods are utilized to dynamically adjust the congestion control rules according to the change of network topologies. The learning-based approach has achieved some promising results. However, there are some issues that require further discussions.