## 1 Introduction  
As connected vehicles become increasingly prevalent, managing traffic flows efficiently is crucial to preventing congestion. Intelligent transportation systems (ITS) and V2X communications play vital roles in achieving effective, secure, and sustainable transportation. 5G-NR-V2X sidelink communications have emerged as a notable solution for facilitating direct and dependable communication between proximate vehicles, without the need for a base station or cellular network. Despite its advantages, supervising communication within 5G-NR-V2X sidelink networks presents challenges due to the dynamic nature of vehicular traffic and the potential for congestion. Various congestion control methods have been proposed to tackle these challenges, including DCC algorithms, which aim to optimize network resource utilization while maintaining fairness and stability [1]. DCC, a cross-layer mechanism standardized by the European Telecommunications Standards Institute (ETSI), is extensively utilized in the ITS sector [2]. However, there remains potential for enhancing its performance in terms of improved network utilization, minimized packet loss, and equitable bandwidth allocation. This paper focuses on investigating and developing an optimized DCC algorithm for 5G-NR-V2X sidelink communications within the context of ITS. The main objective is to augment the performance of DCC algorithms by integrating transmission power control (TPC) and transmission rate control (TRC) methods into a hybrid control framework [3]. By combining the strengthsof both TPC and TRC, this approach aims to facilitate superior network utilization and congestion management in vehicular networks, leading to more efficient and sustainable transportation systems. Within the ITS context, the exchange of cooperative awareness messages (CAMs) and event-triggered decentralized environmental notification messages (DENMs) plays a vital role [4]. Periodically broadcast CAMs convey essential information such as vehicle position, speed, acceleration, and other relevant data, which are crucial for safety and traffic efficiency applications. To address hazardous road situations promptly, DENMs are also required. Both CAMs and DENMs are transmitted on the control channel (CCH), dedicated to cooperative road safety [5]. However, the use of a shared control channel can lead to radio congestion in scenarios with high vehicle density. To address this issue, the DCC framework is introduced as a solution for alleviating control channel congestion. The DCC framework achieves this by regulating the message rate, transmission power, and data rate of periodic messages. Operating as a cross-layer mechanism, the DCC encompasses congestion control at both the network and MAC layers, utilizing information from the physical and network layers to manage congestion effectively. The protocol employs a sliding window mechanism, dynamically adjusting the window size based on the network congestion level. The DCC protocol features two distinct approaches for managing congestion control: adaptive and reactive [3]. The DCC Adaptive approach, prescribed by the ETSI, adaptively adjusts DCC parameters based on real-time evaluations of network conditions [5]. By assessing the current network congestion status using gathered metrics, this method modifies communication parameters according to the evaluation results. In contrast, the DCC Reactive approach is designed to respond to specific network events or triggers as shown in Figure 1. Instead of proactively monitoring and adjusting communication parameters based on real-time network conditions like the DCC Adaptive approach, the DCC Reactive approach operates through a set of predefined rules or policies. Upon detecting a triggering event, this reactive method promptly takes action to mitigate congestion by adjusting communication parameters, such as transmission power, packet size, or transmission intervals.