  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.
