Dynamic Resource Allocation for 5G-Enabled Industrial Internet of Things System with Delay Tolerance
Heng Wang, Yixuan Bai, Xin Xie · 2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall) · 2022
With its low delay and high reliability, 5G technology can meet the requirement of interconnection for Industrial Internet of things (IIoT). However, industrial heterogeneous networks have different quality of service (QoS) requirements, so 5G slicing technology is necessary to logically isolate them from each other. A main challenge lies in how to reasonably allocate network resources for each slice and the nodes in it to ensure the low delay and reliability requirements of IIoT. In this paper, we focus on the problem of minimizing the usage of physical resource blocks (PRBs) under delay constraints, and propose a dynamic allocation algorithm based on traffic prediction. In order to analyze the spatial and temporal features of network traffic, we combine convolutional neural network (CNN) with bidirectional long short-term memory (Bi-LSTM), and then add attention mechanism to form a traffic prediction model CNN-Bi-LSTM with attention mechanism (CBL-A). For dynamic resource allocation, according to the traffic prediction results, on the basis of Dueling Double DQN (D3QN), a heuristic PRBs scheduling policy (PSP) is embedded to obtain a D3QN model with PSP (D3QN-PSP), which reduces the action space and accelerates the convergence speed. The simulation results show that the proposed algorithm can minimize the consumption of PRBs while guaranteeing the delay and slice isolation constraints.