# Energy-Efficient Resource Allocation Based on Deep Q-Network in V2V Communications
## 2 Related Work
Graph neural networks (GNNs) have achieved some success in solving resource allocation problems in various communication networks, e.g., wireless networks, wired networks, and software-defined networks, because of their abilities to learn to capture the dependencies of graphs and to learn non-Euclidean structure data [21]. The authors of [22] presented a comprehensive review and analysis of graph-based resource allocation methods in cellular, device-to-device, and cognitive radio networks. Here, they classified the graph-based resource allocation methods in terms of graph models, tasks solved via graphs, graph formulation, and optimization methods. The authors of [23] proposed a heterogeneous bipartite GNN (HBGNN) to solve the joint user association and power allocation problem in heterogeneous ultra-dense networks (HUDNs). They modeled the downlink of the HUDN as a heterogeneous bipartite graph and compared the performance of the proposed HBGNN with the fully connected neural network and the convolutional neural network (CNN). However, the HBGNN requires supervised learning, unlike RL. The authors of [24] developed a graph convolutional network (GCN)-based DRL framework to perform joint channel selection and power adaptation in the underlying cognitive radio networks, maximizing the data rate of secondary users while maintaining the level of interference to primary users. They modeled the environment of the cognitive radio network as a dynamic graph and adopted a DRL to explore the optimal resource allocation strategy. However, the work of [24] did not take the energy efficiency and the latency constraints of the secondary users into account. The authors of [7] presented a GNN-augmented RL method to perform spectrum allocation for vehicular networks. They expressed the V2V network as a graph and exploited RL to perform resource allocation. The deep Q-network was developed to select the spectrum for each V2V pair. Deep learning technologies for Internet of vehicle (IoV) networks have been studied previously [25; 26; 27; 28; 29]. The authors of [25] discussed deep learning applications for security and collision prediction in the internet of vehicle (IoV) networks, and they proposed a DRL-based resource allocation method to enhance multiple QoS requirements, such as latency and suitable data rate requirements. They introduced an actor-critic framework to achieve an intelligent resource allocation in the IoV network. The authors of [26] discussed deep learning techniques to enhance the performance of the overall IoV system. They addressed various learning networks, e.g., CNN, recurrent neural networks, DRL, classification, clustering, and regression. The authors of [27] presented a comprehensive review and analysis of machine learning technologies for IoV applications, e.g., energy- and buffer-aware optimization, edge caching, intelligent decisions for network schedulingand adaptation, intelligent autonomous driving, etc. The authors of [28] presented a comprehensive review of resource allocation and management for the IoV over 5G radio access networks. They described learning-based resource allocation approaches to improve the QoS and quality of experience in distributed and cloud-computing resource allocation schemes, along with big data resource allocation. The authors of [29] conducted a critical review and analysis of machine learning models used to resolve the challenge in IoV applications. Moreover, they proposed a Markov decision-process-based, edge-computing offloading model and evaluated its performance in terms of its power consumption and task latency. Moreover, vehicular edge computing (VEC) technologies have been studied to dynamically manage computing resources, caching, and networking [30]. The authors of [31] proposed a generic approach to improve the performance of application outsourcing in the caching-assisted VEC. They mathematically showed that application caching can optimize the average response time while satisfying the long-term energy consumption constraint.