# Deep Reinforcement Learning for Energy-Efficient 6G V2X Networks
## 2 Related Work
### Traditional Optimization Approaches to Minimize Energy Consumption and Latency  
Traditional (conventional) optimization is employed to minimize energy consumption and latency in the resource allocation problem. [PERSON] et al. [11] formulated an ILP resource management problem to minimize the number of active remote radio heads (RRHs) subject to uplink data rate constraints and V2X QoS requirements. They solved the resource allocation problem using an optimal algorithm to appropriately associate vehicles with RRHs. They proposed heuristic algorithms to deal with the complexity of large-size scenarios. [PERSON] et al. [27] proposed a novel low-complexity distributed algorithm to minimize average latency and energy consumption in a vehicular edge computing system. They used the alternating-direction method of multipliers for equal resource allocation using distributed latency-aware collaborative computing. They used distributed energy-aware collaborative computing to optimize the partial offloading ratio and the local computation resource allocation ratio.  
[PERSON] et al. [28] proposed a two-tier task offloading structure for a multiuser multi-server edge Internet of things (IoT) system. They formulated an optimization problem to minimize task processing latency and energy consumption. They solved the minimization problem by combining a genetic algorithm (GA) and particle swarm optimization (PSO) computation offloading algorithms to perform global and local searches. [PERSON] et al. [29] formulated a joint optimization problem to minimize total task response times and communication energy while satisfying computational energy constraints. They developeda decentralized convex optimization approach to decompose a holistic MINLP into a hierarchy of convex optimization problems. The optimization problem is decoupled into two subproblems: resource allocation and task offloading. To achieve a near-optimal solution, the resource allocation subproblem is solved in a decentralized manner, while task offloading is solved using a probability-based solution.  
[PERSON] et al. [30] proposed a traffic model based on a stochastic geometry framework to simulate a real traffic scenario of autonomous driving vehicles. They formulated an average-cost minimization problem to reduce the processing task cost in a distributed computation offloading environment based on MEC. The optimization problem is divided into several subproblems and is solved using Lagrange multipliers with Karush-Kuhn-Tucker (KKT) constraints. [PERSON] et al. [38] introduced a joint computation offloading and resource allocation strategy to build green V2X networks with MEC and integrated sensing and communication (ISAC) technologies. They formulated an optimization problem to minimize the queuing latency of computing tasks while satisfying the constraints on latency and energy consumption constraints. The minimization problem is reformulated using the Lyapunov optimization method to transfer latency and energy constraints to queue stability problems. A joint computation offloading and resource allocation (JCORA) scheme is proposed to reduce energy consumption under the task latency constraint.