# A Reinforcement Learning-Based Congestion Control Approach for V2V Communication in VANET
## 2 Related Work
D-FPAV achieves congestion control by setting the node transmission power based on the prediction of application-layer traffic and the observed number of vehicles in the surrounding area. As a proactive approach, D-FPAV uses a predefined maximum beaconing load (MBL) and calculates the network-wide optimal transmission power to keep the channel load under this value. By periodically gathering the information about AL (Application-layer Load) for nodes within the maximum carrier sense range, a node first uses FPAV [22] to calculate the maximum transmission power for all the nodes within its maximum carrier sense range, without violating the MBL condition. Then the nodes exchange this power by broadcasting to the neighbors within the maximum carrier sense range. After getting the same message from the nodes in its vicinity, each node computes the final power level, which is the minimum value among the local maximum transmission power levels in its vicinity. In FPAV, all the nodes increase their transmission power simultaneously, by the same amount, as long as the MBL condition is satisfied. Both D-FPAV and FPAV rely on predicting the MBL, which becomes increasingly challenging as vehicle density increases. The adaptive beacon transmission power (ABTP) algorithm [23] firstly improves the traditional linear prediction algorithm and elaborates a linear combination model to reduce the vehicle's turning error. The approach adjusts transmission power based on vehicle position prediction error, increasing power for vehicles with large errors and reducing it for vehicles with small errors. The paper lacks a mechanism for evaluating distance error, such as identifyingwhen the distance error is 10 m. Another limitation of the study is that it sets a maximum beacon load of 70%, which can be surpassed with high vehicle density. The DSRC standard defines seven data rates, ranging from 3 Mbps to 27 Mbps, with 10 MHz channels, according to the reference in [7]. The goal of data rate control is to increase the rate by using higher modulation levels, which reduces channel load and collisions. Most studies in the literature rely on the findings of [24], which suggests that 6 Mbps is optimal for most cases. However, this study is not dynamic and the transmit power levels are not clear. The binary rate algorithm described in [25] is a data-rate control algorithm that dynamically adjusts the data rate based on the channel load. The algorithm utilizes a state machine with three states: relaxed, active, and restrictive. Each state has different transmission parameters, and the vehicle detects the current state based on the channel load and uses the corresponding parameters to send messages. It is worth noting that this algorithm can only switch between four data rates. In [26], the authors propose a new approach that dynamically selects an appropriate data rate for each BSM transmission, based on the current CBR. Unlike existing algorithms that typically increment the data rate only one level at a time, regardless of how high the CBR is, the proposed algorithm directly estimates the appropriate data rate to use based on the current CBR value. This allows the channel congestion to converge to the desired level much faster, leading to lower packet loss and improved packet delivery ratio. Similarly, when the current CBR is below the desired threshold, the proposed algorithm calculates the appropriate data rate and starts transmitting directly using these data rates, rather than moving through intermediate levels. This approach uses a fixed threshold of CBR, which could vary dynamically. Despite these efforts, the challenge of achieving higher signal-to-interference noise ratios (SINRs) and meeting safety range constraints for higher data rates remains. As a result, data rate control is always combined with other transmission parameters for congestion control in VANET. The ultimate goal of congestion control is to enhance vehicle awareness. While decreasing the transmission power or rate may alleviate congestion, it reduces awareness quality.