On-Demand Resource Management for 6G Wireless Networks Using Knowledge-Assisted Dynamic Neural Networks
This addresses resource management challenges for 6G networks, but it appears incremental as it builds on existing neural network approaches with dynamic adjustments.
The paper tackles the problem of on-demand resource orchestration in 6G wireless networks by optimizing decision-making delay, proposing a dynamic neural network method that adjusts model complexity based on service requirements and uses a knowledge base to improve performance. Simulation results show it significantly outperforms traditional static neural networks and offers flexibility in service provisioning.
On-demand service provisioning is a critical yet challenging issue in 6G wireless communication networks, since emerging services have significantly diverse requirements and the network resources become increasingly heterogeneous and dynamic. In this paper, we study the on-demand wireless resource orchestration problem with the focus on the computing delay in orchestration decision-making process. Specifically, we take the decision-making delay into the optimization problem. Then, a dynamic neural network (DyNN)-based method is proposed, where the model complexity can be adjusted according to the service requirements. We further build a knowledge base representing the relationship among the service requirements, available computing resources, and the resource allocation performance. By exploiting the knowledge, the width of DyNN can be selected in a timely manner, further improving the performance of orchestration. Simulation results show that the proposed scheme significantly outperforms the traditional static neural network, and also shows sufficient flexibility in on-demand service provisioning.