论文标题
考虑到C-V2X安全性的频谱和能源效率的联合优化：一种深厚的增强学习方法
Joint Optimization of Spectrum and Energy Efficiency Considering the C-V2X Security: A Deep Reinforcement Learning Approach
论文作者
论文摘要
作为5G无线通信的一部分，蜂窝车辆到所有的通信（C-V2X）被认为是智能城市最重要的技术之一。车辆排是智能城市的应用，可通过C-V2X提高交通能力和安全性。但是，与在高速公路上行驶的车辆排不同，C-V2X可以更容易地窃听，并且当它们在交叉路口汇聚时，频谱资源可能会受到限制。满足C-V2X的保密速率，如何提高谱系网络中的光谱效率（SE）和能源效率（EE）是一个巨大的挑战。在本文中，为了解决这个问题，我们提出了一种基于深厚的增强学习，名为SEED的安全感知方法来增强SE和EE。种子可以考虑SE和EE的客观优化函数，并且C-V2X的保密率被视为该功能的关键约束。使用深Q网络（DQN）将优化问题转换为V2V和V2I链接的频谱和传输功率选择。 SE和EE的启发式结果由DQN政策基于奖励获得。最后，我们使用Python模拟了流量和通信环境。评估结果表明，种子的表现优于DQN-WOPA算法，而基线算法的效率为31.83％和68.40％。种子的源代码可在https://github.com/bandaidz/optimizationofseandeebasedondrl上获得。
Cellular vehicle-to-everything (C-V2X) communication, as a part of 5G wireless communication, has been considered one of the most significant techniques for Smart City. Vehicles platooning is an application of Smart City that improves traffic capacity and safety by C-V2X. However, different from vehicles platooning travelling on highways, C-V2X could be more easily eavesdropped and the spectrum resource could be limited when they converge at an intersection. Satisfying the secrecy rate of C-V2X, how to increase the spectrum efficiency (SE) and energy efficiency (EE) in the platooning network is a big challenge. In this paper, to solve this problem, we propose a Security-Aware Approach to Enhancing SE and EE Based on Deep Reinforcement Learning, named SEED. The SEED formulates an objective optimization function considering both SE and EE, and the secrecy rate of C-V2X is treated as a critical constraint of this function. The optimization problem is transformed into the spectrum and transmission power selections of V2V and V2I links using deep Q network (DQN). The heuristic result of SE and EE is obtained by the DQN policy based on rewards. Finally, we simulate the traffic and communication environments using Python. The evaluation results demonstrate that the SEED outperforms the DQN-wopa algorithm and the baseline algorithm by 31.83 % and 68.40 % in efficiency. Source code for the SEED is available at https://github.com/BandaidZ/OptimizationofSEandEEBasedonDRL.
