With the rapid development of Internet of vehicles (IoV) technology, the distribution of vehicles on the highway becomes more dense and the highly reliable communication between vehicles becomes more important. Nonorthogonal multiple access (NOMA) is a promising technology to meet the multiple access volume and the high reliability communication demands of IoV. To meet the Vehicle-to-Vehicle (V2V) communication requirements, a NOMA-based IoV system is proposed. Firstly, a NOMA-based resource allocation model in IoV is developed to maximize the energy efficiency (EE) of the system. Secondly, the established model is transformed into a Markov decision process (MDP) model and a deep reinforcement learning-based subchannel and power allocation (DSPA) algorithm is designed. An event trigger block is used to reduce computation time. Finally, the simulation results show that NOMA can significantly improve the system performance compared to orthogonal multiaccess, and the proposed DSPA algorithm can significantly improve the system EE and reduce the computation time.

中文翻译：

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支持 NOMA 的车联网的节能资源分配

随着车联网（IoV）技术的快速发展，高速公路上车辆的分布变得更加密集，车辆之间的高可靠通信变得更加重要。非正交多路访问（NOMA）是一种很有前途的技术，可以满足车联网的多路访问量和高可靠性通信需求。为了满足车对车 (V2V) 通信要求，提出了基于 NOMA 的 IoV 系统。首先，开发了基于 NOMA 的车联网资源分配模型，以最大限度地提高系统的能源效率 (EE)。其次，将建立的模型转化为马尔可夫决策过程（MDP）模型，设计了一种基于深度强化学习的子信道和功率分配（DSPA）算法。事件触发器块用于减少计算时间。最后，