考虑C-V2X安全性的频谱和能量效率联合优化：一种深度强化学习方法
Joint Optimization of Spectrum and Energy Efficiency Considering the C-V2X Security: A Deep Reinforcement Learning Approach
摘要：
蜂窝车联网（C-V2X）通信作为5G无线通信的一部分，被认为是智慧城市最重要的技术之一。车辆排队是智慧城市的一种应用，通过C-V2X提高交通容量和安全性。然而，与在高速公路上排队行驶的车辆不同，C-V2X更容易被窃听，并且当它们在交叉路口汇合时，频谱资源可能会受到限制。在满足C-V2X保密性的前提下，如何提高队列网络的频谱效率和能量效率是一个很大的挑战。在本文中，为了解决这个问题，我们提出了一种基于深度强化学习的增强SE和EE的安全感知方法，称为SEED。该算法建立了一个同时考虑安全性和效率的目标优化函数，并将C-V2X的保密率作为该函数的关键约束。使用深度Q网络（DQN）将优化问题转化为V2 V和V2 I链路的频谱和发射功率选择。通过基于奖励的DQN策略得到了SE和EE的启发式结果。最后，我们使用Python模拟了交通和通信环境。实验结果表明，SEED算法的效率比DQN-wopa算法和baseline算法分别提高了31.83%和68.40%。SEED的源代码可在https://github.com/BandaidZ/OptimizationofSEandEEBasedonDRL上获得。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
