Deep Reinforcement Learning Based Resource Allocation in Delay-Tolerance-Aware 5G Industrial IoT Systems

Abstract

With the widespread application of 5G technology in the Industrial Internet of Things (IIoT), dividing nodes into different network slices according to delay tolerance requirements can facilitate reasonable resource allocation and guarantee quality of service (QoS). In this paper, we investigate the network slice resource allocation algorithm with delay tolerance based on traffic prediction. A traffic prediction algorithm is proposed combining convolutional neural network (CNN) with attention mechanism and bidirectional long-short term memory (Bi-LSTM) to obtain the spatiotemporal features. Based on the predicted traffic, the problem of minimizing the usage of physical resource blocks (PRBs) is studied, and a two-layer structure resource allocation algorithm based on deep reinforcement learning (DRL) is proposed. Specifically, Dueling Double DQN (D3QN) is used to allocate PRBs between slices, and a heuristic algorithm is used to allocate PRBs among nodes in the slice. Furthermore, we consider the joint optimization problem of PRBs and power. In light of the coupling between PRBs and power aggravates the high dimension of the action space, we propose a resource allocation algorithm which using the branch structure to decoupling the action space. Simulation results show that the proposed algorithms can satisfy the QoS and outperform the baseline algorithms.