MobileNet and Knowledge Distillation-Based Automatic Scenario Recognition Method in Vehicle-to-Vehicle Systems
Jie Yang, Yu Wang, Haitao Zhao, Guan Gui
Abstract
Automatic scenario recognition (ASR) based on channel status information (CSI) is an important auxiliary technology for various wireless communication systems, especially vehicle-to-vehicle (V2V) systems. CSI-based ASR methods, only based on the on-board wireless receivers rather than other expensive sensors, can identify the surrounding environments of vehicles to serve assisted and autonomous driving technologies. Recently, deep learning (DL)-based ASR methods have been proposed, which generally outperform traditional, but they generally have high computation complexity and large model sizes, which are hardly introduced into mobile edge devices, such as smart vehicles. In this paper, a lightweight convolutional neural network design method, inspired by MobileNet, is introduced into ASR for reducing computation complexity and model sizes. In addition, this method may bring about performance loss, and knowledge distillation (KD) is adopted to make up for this performance loss. Simulation results demonstrated that our proposed ASR method just has 30% computation complexity and model size of the original method, and its performance loss is very limited, especially after KD.