# Energy-Efficient Resource Allocation Based on Deep Q-Network in V2V Communications
## 2 Related Work  
The resource allocation mechanism in vehicle-to-everything (V2X) communications has been studied in various ways. The authors of [11] introduced the deep reinforcement learning (DRL)-based resource allocation scheme and showed experimental results for both unicast and broadcast scenarios. They designed a reward function to ensure the latency constraints of the V2V links were satisfied. The authors of [12] proposed a QoS-aware resource allocation scheme based on the DRL framework in V2X communications, where they took QoS parameters such as the priority of V2V messages into consideration. The proposed scheme of [12] aims to maximize the sum rate of vehicle-to-infrastructure (V2I) links while satisfying the latency constraints of V2V links. The authors of [13] developed a power allocation problem in the cellular device-to-device (D2D)-based V2X communication network and mathematically solved the problem. They showed that the proposed power allocation scheme outperforms the existing algorithms in terms of power consumption. The authors of [14] developed a multi-agent RL (MARL)-based resource allocation for V2V links in the spectrum-sharing V2X network. They aimed to maximize the capacity of V2I links while also improving the reliability of the payload delivery in V2V links. They showed the MADRL-based resource allocation is efficient for the V2I and V2V network collaboration although decisions are made locally and distributed at each V2V transmitter. The authors of [15] proposed a MARL-based resource allocation scheme in order to maximize the sum rate of V2I links while satisfying the latency and reliability requirements of V2V links. In this work, they developed individual double-dueling deep recurrent Q-networks (D3 RQN), where they used interference power measurements instead of the conventional CSI under the assumption that it is difficult to acquire the perfect CSI in the vehicular network. They showed that the proposed MARL-based resource allocation jointly adjusts the sub-channel and transmit power using only local interference measurements without inter-agent communication. Some studies have focused on the energy consumption in V2X networks. The authors of [16] developed an energy efficiency problem in an NR V2X network, where energy efficiency is defined as the ratio of the sum rate to power consumption. They proposed a heuristic algorithm of traffic-density-based random selection to solve the developed mixed-integer problem. The authors of [17] also developed an energy efficiency problem of vehicle users while considering the QoS requirement of cellular users in the cellular network underlying V2V communications. They transformed the latency constraint into the constraint of the queue length and solved the virtual queue problem based on the Lyapunov optimization. In V2X networks, the energy consumption of V2I links as well as V2V links is important. Some studies have focused on optimizing energy consumption across the entire wireless access network [18; 19; 20]. The authors of [18] proposed an energy-efficient resource management scheme based on the transmit power scaling and on/off switching of base stations. The authors of [19] formulated an optimization problem for the energy consumption of a wireless location area network (WLAN) by adjusting the transmit power and turning access stations on and off based on realistic traffic patterns. They proposed integer linear programming (ILP) optimization models and heuristic algorithms to minimize the energy consumption of the network. The authors of [20] developed an ILP model for energy saving of wireless access networks, and also developed a heuristic algorithm based on a greedy method to cope with the computational complexity of the ILP model. Recently, graph-based deep learning solutions have been proposed for resource allocation in communication networks [7; 21; 22; 23; 24].