# Data Rate Selection Strategies for Periodic Transmission of Safety Messages in VANET
## 2 Background Review
### Power- and Message Rate-Based Approaches
[40], the authors proposed a new congestion control strategy called periodically updated load-sensitive adaptive rate control (PULSAR), where vehicles measure the current CBR at regular intervals and compare it with a predefined target value. The vehicle would lower its transmission rate if the measured CBR was greater than the target value. This method would result in keeping the CBR below the target value in order to control channel congestion. In Ref. [41], the vehicles send packets which have beacon transmission rate (BTR) adjustment requests attached, which tell the receiving vehicles to raise or lower their BTR. This results in the vehicles broadcasting their beacons using varying message rates. In Ref. [42], the beacon messages were scheduled according to the priorities and transmission power. Messages were dequeued automatically according to the priority queue model. In Ref. [43], the congestion control scheme adjusts the message rate as it adapts to changes in the local vehicle density. In Ref. [44], the authors propose a non-cooperative game approach where each vehicle behaves as a selfish node, requesting high beacon rates. A utility function, based on vehicle priority and contention delay, is used to obtain the optimal rates. In Ref. [45], the authors propose a congestion control algorithm that adjusts the rate for safety message transmissions based on traffic density. A car ID-based randomized back-off code is used to reduce the likelihood of collision due to identical back-off numbers. In Ref. [46], the authors assign a weight to each node based on quality of vehicle links and then use a greedy algorithm to assign suitable beacon rates to nodes based on the weights. In Ref. [47], a novel approach that investigates beacon transmission from the perspective of information loss is introduced, and a greedy rate adjustment algorithm to minimize information loss is presented.The work in Ref. [48] formulates message rate selection as a Markov decision process (MDP), and the vehicle learns the appropriate rate to use under different conditions, using a reinforcement learning algorithm. Some recent congestion control approaches have suggested methods that involve a combination of multiple control parameters, such as both transmission power and message rate, instead of just one, to effectively reduce the channel load. These are called hybrid approaches. In Ref. [49], two transmission power levels were used, one for high power and one for low power. A designated proportion of the BSMs sent by each vehicle would be sent with high power, while the rest would be sent using low power. Combined with LIMERIC, the proposed method used transmission power and message rate to reduce congestion and yielded a low BER. In Ref. [50], the authors proposed the combined power and rate control (CPRC) algorithm, which adjusted the message rate and transmission power in a single loop of the algorithm. CPRC resulted in cooperative behavior among the vehicles, as it increased the message rate of vehicles involved in a potentially dangerous situation while decreasing the transmission power of the vehicles that were not directly involved. As a result, this prevented the channel load from exceeding a predefined threshold value. In Ref. [51], the authors proposed the random transmission power control (RTPC) algorithm, which was combined with transmit rate control (TRC). This method involved increasing the message rate until the target channel load value was reached. In Ref. [52], the authors proposed a cooperative game theory-based approach to jointly control the beacon rate and transmission power. In this approach, each vehicle controls its own share of the bandwidth based on its application requirements. The work in Ref. [53] uses different machine learning algorithms to predict the vehicle density, and then adjusts the vehicle's transmission rate and power accordingly to ensure awareness and reduce channel load. In Ref.