Machine Learning based Resource Allocation Strategy for Network Slicing in Vehicular Networks

To deal with the lack of prediction and management for vehicular network slice in existing research, this paper designs a machine learning based resource allocation strategy for vehicular network slicing. Firstly, a traffic prediction mechanism based on Convolutional Long Short-Term Memory (ConvLSTM) is proposed, which will capture the spatial-temporal dependencies of the traffic to predict traffic of complex slice services in the vehicular networks. Secondly, considering the imbalance of wireless resource utilization caused by the space-time difference between application scenarios, a shared proportional fairness scheme is proposed to achieve efficient and differentiated utilization of wireless resources. Finally, on the basis of ensuring the demand of each slice, the resource allocation algorithm based on the primal-dual interior-point method is used to solve the optimal slice weight allocation to minimize the system delay. Simulation results show that the service traffic prediction mechanism can be used to predict service traffic in the future. The average error rates of SMS, phone, and web traffic will be reduced, so that the user load distribution can be obtained a priori. Based on the predicted load distribution, slice weight distribution is performed in advance so that arranging delay is saved. The resource allocation algorithm based on the primal-dual interior-point method can well calculate the optimal slice weight distribution at this time.