Abstract
The diversified service requirements in vehicular networks have stimulated the investigation to develop suitable technologies to satisfy the demands of vehicles. In this context, network slicing has been considered as one of the most promising architectural techniques to cater to the various strict service requirements. However, the unpredictability of the service traffic of each slice caused by the complex communication environments leads to a weak utilization of the allocated slicing resources. Thus, in this paper, we use Long Short-Term Memory- (LSTM-) based resource allocation to reduce the total system delay. Specially, we first formulated the radio resource allocation problem as a convex optimization problem to minimize system delay. Secondly, to further reduce delay, we design a Convolutional LSTM- (ConvLSTM-) based traffic prediction to predict traffic of complex slice services in vehicular networks, which is used in the resource allocation processing. And three types of traffic are considered, that is, SMS, phone, and web traffic. Finally, based on the predicted results, i.e., the traffic of each slice and user load distribution, we exploit the primal-dual interior-point method to explore the optimal slice weight of resources. Numerical results show that the average error rates of predicted SMS, phone, and web traffic are 25.0%, 12.4%, and 12.2%, respectively, and the total delay is significantly reduced, which verifies the accuracy of the traffic prediction and the effectiveness of the proposed strategy.
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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