## 2 Related Works  
Beaconing is the process of regularly broadcasting the kinematics of sending a vehicle to prevent V2V collisions. Two key parameters of beaconing are the BR and TP. The BR refers to the frequency at which these broadcast messages are sent, typically measured in Hertz (Hz), while TP determines the range over which the beacon messages can be effectively received. Beaconing faces two major challenges: updating vehicle kinematics promptly for better awareness under unreliable wireless channels, and maintaining an appropriate channel busy ratio (CBR) to avoid both channel underutilization and excessive message collisions. Although the beaconing problem has been extensively studied in the last two decades [6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20], these studies do not fulfill the requirements of safety applications, as shown in Figure 2. The seminal work in [6] presents a fair TP control algorithm, ensuring that the beacon load within a two-hop distance is limited to a given threshold. However, we believe that providing equal channel access opportunities without considering the varying kinematics of vehicles may not achieve true fairness at the application layer. Figure 2: Comparison of beaconing approaches: TPC [6], BRC [7; 8; 9; 10], JCBRTP [11; 12; 13; 14], STE [15; 16], and DRL-JCBRTP. (Green: Access-layer approaches, Yellow: Partial cross-layer approaches, and Red: Full cross-layer approaches). To address the challenge of channel congestion through BR control, a few BR control (BRC) schemes have been proposed in the literature [7; 8; 9; 10]. A new adaptive BRC algorithm is proposed to ensure fair and efficient channel utilization in [7]. The authors in [8] present a simple algorithm to determine the beacon interval for stable CBR against dynamic beacon loads. A priority-based scheme increases the BR for vehicles with increased collision risks in terms of time-to-collision [9]. A game theoretic approach in [10] modifies the BR using risk factors, aiming to improve adaptability and safety. A common limitation of BRC approaches is their absence of TP control, which can hinder their ability to effectively maintain the CBR within an optimal range. A JCBRTP approach is proposed to efficiently control the channel load by jointly managing the BR and TP [11; 12; 13; 14]. The ETSI decentralized congestion control scheme in [11] jointly controls the BR, TP, data rate, and receiver sensitivity to keep the CBR in a desirable range. A non-cooperative game-theoretic approach, called the beacon frequency and power control (BFPC) scheme, is proposed in [12] to find the Nash equilibrium of BR and TP in a distributed manner. In addition, the Markov decision process with a rate and power (MDPRP) framework employs Q-learning to derive the optimal policy for BR and TP that keeps the CBR in a desirable range [13]. Furthermore, the paper in [14] addresses the joint power-rate optimization problem for the Rayleigh fading channel by solving its Lagrangian dual problem. Although improving the access-layer performance indirectly enhances awareness, a key limitation of the BRC approach is its weak connection to application-layer requirements, such as the detection and tracking of neighbor vehicles. Assuming that each vehicle has a remote kinematics estimator to estimate the kinematics of each sending vehicle based on its most recently received beacon information, the beaconing schemes in [15; 16] address the above limitations by devising an indirect tracking metric, called the suspected tracking error (STE), to assess the average tracking error between the real and estimated kinematics of the sending vehicle. These schemes control the BR to reduce the STE metric while maintaining the CBR within a desirable range. However, they do not account for the variation in tracking errors among neighbor vehicles, which often fails to limit their tracking errors to a tracking threshold \(E_{T}\).