## 2 Related Works
[36] addressed the challenges of minimizing energy consumption and data communication delay in vehicular networks, with a particular focus on the dynamic and time-varying nature of wireless channels and bandwidth. Leveraging MEC servers associated with BSs, vehicles, and RSUs can offload computing tasks to enhance processing efficiency and reduce delays. However, the unstable environment for offloading tasks to the MECS, characterized by varying wireless channel states and available bandwidths, poses significant challenges. To tackle this, the researchers proposed a task computation offloading model for heterogeneous vehicular networks considering multiple stochastic tasks and the variability of wireless channels and bandwidth. To address the complexity of the large action space and obtain a tradeoff between energy consumption and data transmission delay, they introduced an adaptive computation offloading method based on deep reinforcement learning (ACORL). ACORL utilizes the Deep Deterministic Policy Gradient (DDPG) as a deep reinforcement learning method to optimize the offloading policy, efficiently addressing continuous action space; additionally, the method incorporates improvements to the Ornstein-Uhlenbeck (OU) noise vector for effective stochastic exploration. Through extensive simulations under varying channel states and available bandwidth scenarios, the researchers demonstrated the effectiveness of ACORL in adaptively performing local execution and offloading tasks to MEC servers, outperforming two baseline schemes. However, while this work makes significant strides in addressing the challenges of computation offloading in vehicular networks, the evaluation was primarily conducted through numerical simulations; further validation through real-world experiments or field trials could provide more insights into the practical applicability and robustness of the proposed approach. [PERSON] et al. [37] proposed a novel approach to address the challenges in end-to-end low-latency transmission and backhaul resources in 5G-enabled V2X networks by leveraging MEC. By considering the crucial factors of reliability and delay in vehicle communication, the authors introduced a joint computation and Ultra-Reliable Low-Latency Communication (URLLC) resource allocation strategy for collaborative MEC-assisted cellular-V2X networks. To tackle the NP-hard optimization problem, the authors decomposed it into two subproblems: URLLC resource allocation and computation resource decisions. They further introduced non-cooperative game theory and bipartite graph techniques to reduce inter-cell interference and optimize channel allocation for URLLC V2X communication. Additionally, an online Lyapunov optimization method was proposed to achieve a tradeoff between average weighted power consumption and delay, with the CPU frequency calculated using the Gauss-Seidel method. Simulation results demonstrated the superiority of the proposed strategy over centralized MEC-assisted V2X approaches, achieving better performance in terms of power consumption, overflow probability, and execution delay. While the paper presents a comprehensive strategy for addressing computation and URLLC resource allocation in C-V2X networks, it needs real-world experimental validation. Conducting experiments in real-world settings would help to validate the effectiveness and feasibility of the proposed strategy under various scenarios and environmental conditions. [PERSON] et al. [38] offered a comprehensive overview of optimization methods employed in computation offloading within Edge Computing networks, addressing the challenges of handling massive data generated by Smart Mobile Devices (SMDs). By exploring six types of optimization methods, including Lyapunov optimization, convex optimization, heuristic techniques, game theory, machine learning, and others, the authors provide insights into their respective objective functions, application areas, offloading methods, evaluation methods, and time complexity.