Model-based Deep Learning Optimization of IEEE 802.11 VANETs for Safety Applications

IEEE 802.11p/bd driven Vehicular Ad Hoc Networks (VANETs) have been investigated for safety-critical applications with high reliability and low transmission latency. However, due to dynamic vehicular environment and various safety applications requiring different quality of service (QoS), a fixed configuration of the communication network parameters performs poorly in terms of balance between QoS and channel spectrum efficiency. This paper proposes a new real-time optimization scheme based on a designated deep learning neural network (DLNN) working with a stochastic model. In the scheme, the stochastic model is adopted to predict the QoS of VANET given a set of communication parameters. The DLNN is trained to approach the inverse maps from the parameter sets to the corresponding QoS by a sampled data set from running the stochastic model. In the process of optimization, for a given safety service, the DLNN and the stochastic model complement each other to find an optimal solution of the parameters that maximize the channel efficiency under the constraints of QoS requirements in a fast and precise way. The experiments on Google Colab with TensorFlow and Python demonstrate the effectiveness of the scheme.