SI-AMC: Integrating DL-Based Scenario Identification into Adaptive Modulation and Coding in Vehicular Communications

Zhengpeng Wang, Yanqun Tang, Shiyu Song, Haibo Chen, Xianjie Lu, Fan Liu

Abstract: The key to friendly collaboration in vehicular communication systems lies in the reliable communication between vehicles. The current systems, which employ fixed transmission schemes, significantly constrain system capacity in respect of spectrum. Besides, considering the dynamic environment of vehicular communications, the requirement for real-time scenario identification is particularly urgent. Hence, in order to improve the reliability of communications, this paper proposes an adaptive modulation and coding (AMC) technique driven by deep learning (DL)-based scenario identification (SI) in vehicular communication systems, namely SI-AMC. In contrast to the traditional AMC technique, our proposed SI-AMC attains a refined channel response estimation through the pre-discrimination of scenario features, thereby further enhancing vehicular communication performance. During the transmission process, the SI-AMC scheme achieves environment adaptability through rate-adaptive adjustments. Moreover, in terms of SI, we creatively design an enhanced convolutional neural network structure which ex-ploits a novel activation function and regularization strategies to enhance the robustness of the model. Simulation results show that the accuracy of SI reaches up to 97.86 % and the throughput of the entire link in vehicular communications is effectively promoted. Code and the model will be released at https://github.com/communicationDLl/SI-AMC.