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Cellular vehicle-to-everything (C-V2X) communication, as a part of 5Gwireless communication, has been considered one of the most significanttechniques for Smart City. Vehicles platooning is an application of Smart Citythat improves traffic capacity and safety by C-V2X. However, different fromvehicles platooning travelling on highways, C-V2X could be more easilyeavesdropped and the spectrum resource could be limited when they converge atan intersection. Satisfying the secrecy rate of C-V2X, how to increase thespectrum efficiency (SE) and energy efficiency (EE) in the platooning networkis a big challenge. In this paper, to solve this problem, we propose aSecurity-Aware Approach to Enhancing SE and EE Based on Deep ReinforcementLearning, named SEED. The SEED formulates an objective optimization functionconsidering both SE and EE, and the secrecy rate of C-V2X is treated as acritical constraint of this function. The optimization problem is transformedinto the spectrum and transmission power selections of V2V and V2I links usingdeep Q network (DQN). The heuristic result of SE and EE is obtained by the DQNpolicy based on rewards. Finally, we simulate the traffic and communicationenvironments using Python. The evaluation results demonstrate that the SEEDoutperforms the DQN-wopa algorithm and the baseline algorithm by 31.83 % and68.40 % in efficiency. Source code for the SEED is available athttps://github.com/BandaidZ/OptimizationofSEandEEBasedonDRL.

Joint Optimization of Spectrum and Energy Efficiency Considering the C-V2X Security: A Deep Reinforcement Learning Approach