Industrial Internet Network Slice Prediction Algorithm Based on Multidimensional and Deep Neural Networks

Abstract

In the industrial Internet environment, the introduction of network slicing supports the connection of a large number of devices with different service requirements (QoS) sharing the same physical resources. Aiming at the problem of the adaptability of massive terminal devices and networks in industrial heterogeneous scenarios, this paper proposes a network slice prediction algorithm based on multi-dimensional and deep neural network (MDNN) based on the multi-dimensional resource network requirements of different terminal devices in specific industrial scenarios. The network slice prediction algorithm predicts the network resources required by the device at the next moment according to the historical network requirements and historical slice selection of the device, and selects the appropriate network slice for the device according to the prediction result. The simulation results show that the prediction accuracy of the proposed algorithm can reach 98.70%, which greatly improves the adaptability of the device and the network.