Abstract
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.
Highlights
Autonomous driving is one of the key scenarios in 5G networks
We propose a new radio resource management, namely, shared proportion fairness (SPF), to keep resource management in accordance with slicing vehicle activity, and we use it for resource allocation representation
For slicing resource allocation problem in vehicular networks, this paper proposes an Long Short-Term Memory- (LSTM-)based resource allocation, which contains two phases, i.e., traffic prediction phase and resource allocation phase
Summary
Autonomous driving is one of the key scenarios in 5G networks. In order to achieve road safety of intelligent transportation systems (ITS), the ultrareliable and low-latency communications (URLLC) must be guaranteed in vehicular networks. One important issue in network slicing is the scheduling policy which allocates limited resources dynamically to vehicles with various quality of service (QoS) requirements according to the traffic change and network state. In [15], a novel radio resource slicing framework for 5G networks was proposed; radio resources were allocated to different slices based on reinforcement learning. Motivated by the above analysis, we use machine learning to predict the services traffic of each slice, so as to allocate radio resources to each slice to reduce the delay. We use ConvLSTM, which combines CNN and LSTM, to model the temporal-spatial dependency of the slice service traffic in the vehicular communication networks.
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