## 3 System Design and the Proposed HPR-DCC Methodology
### HPR-DCC Design and Strategies  
In the design of the HPR-DCC algorithm, a three-fold approach is employed to optimize performance and efficiency. Firstly, power adaptation for awareness control is facilitated through the component, which dynamically adjusts the transmission power in accordance with the target awareness range dictated by the application context. By estimating the PL, the algorithm is able to modulate transmission power to satisfy awareness prerequisites, even for vehicles in the 'worst' channels that have not exchanged messages. Secondly, the HPR-DCC incorporates rate control by leveraging the state machine. Owing to its ability to converge towards fair and efficient channel utilization, the state machine regulates the forthcoming message rate to sustain the CBR beneath the predetermined threshold. Lastly, the HPR-DCC combines both power and rate control, adjusting the subsequent transmission power on the basis of the current path loss for each message obtained from neighboring vehicles. This process considers the target awareness percentage when determining the appropriate transmission power level. Simultaneously, the algorithm adapts the rate by considering the existing message rate and channel load, represented by the CBR. This comprehensive approach allows for enhanced adaptability and performance in the context of the HPR-DCC algorithm design and features a simple structure.  
To adjust transmission power in response to congestion control and vehicle demand, the proposed algorithm makes a joint decision by calculating PL and current state CBR for state switching. The specific explanation is as follows:  
Assume that vehicles transmit power at time \(t\): \(P_{i}^{Tx}(t)\); target awareness range of vehicle: \(TR_{e}(t)\); target awareness percentage of vehicle: \(TA_{e}(t)\); and shadowing coefficient: \(S\). For each received message, calculate \(d_{jj}(t)\), the distance between vehicle and \(i\)th neighbor  
Figure 2: Procedure of the HPR-DCC algorithm.  
at time \(t\) when received the message \(j\). Compute \(PLE_{ij}(t)\), the PL for message \(j\) from neighbor, by Equation (1):  
\[PLE_{ij}(t)=\frac{PL(t)}{10\log_{10}\left(\frac{4\Pi t}{\lambda}d_{ij}(t)\right)}, \tag{1}\]  
where \(\lambda\) is the signal wavelength and \(PL(t)\) is calculated by Equation (2):  
\[PL(t)=P_{i}^{Tx}(t)-P_{j}^{Rx}(t), \tag{2}\]  
where \(P_{i}^{Tx}(t)\) represents the transmit power of neighbor \(i\) and \(P_{ij}^{Rx}(t)\) denotes the receive power of \(j\)th message form neighbor \(i\).  
Then, calculate the received power required as Equation (3):  
\[P_{r}(t)=\frac{P_{r}\cdot G_{t}\cdot G_{r}\cdot S}{PL(t)}, \tag{3}\]  
where \(P_{t}\) is the transmitted power, \(G_{t}\) is transmitter antenna gain, and \(G_{r}\) is the receiver antenna gain. Then, set the transmission power for next time \((t+1)\):  
\[P_{sortle}^{Tx}=sort_{\forall i,j\in N}(P_{r}(t+1)), \tag{4}\]  
where \(P_{r}(t+1)\) is calculated as:  
\[P_{r}(t+1)=P_{sortle}^{Tx}[round(TA_{e}*N)]. \tag{5}\]  
In Equations (4) and (5), sort the necessary transmission power to each neighboring node and select the appropriate power level for transmission.  
In the proposed algorithm, state switching is jointly determined by PL and CBR, this joint decision-making facilitates ensuring fairness in policies. Multiple states are established, with each active state allocating the appropriate transmission power and transmission rate according to the current channel conditions [24]. The detailed state transitions are presented in Table 1.