Dynamic Allocation of 5G Transport Network Slice Bandwidth Based on LSTM Traffic Prediction

Abstract

5G networks will be characterized by the extremely wide bandwidth that will be available to the user. A more flexible transmission network is necessary to support the demand generated by the access network to continuously increase network bandwidth. Transport network slicing will be a promising technology to address those challenges. In this paper, we focused on the dynamic resource allocation problem of bandwidth in transport network slices. We introduce a novel LSTM -based traffic-predict dynamic transport network slicing framework (LSTM-TPDTNS). Our approach consists of two phases: the traffic prediction phase and the bandwidth configuration phase. For the first phase, we use long and short memory models to predict traffic. For the second phase, we model our problem as a fractional knapsack problem, and we use greedy algorithms to find approximate solutions. Dynamic allocation of resources to services of different priorities can be realized, thereby improving the service quality and user experience of the entire system.