# On-Demand Resource Management for 6G Wireless Networks Using Knowledge-Assisted Dynamic Neural Networks

arXiv: https://arxiv.org/abs/2208.01785 · HTML: https://mlsanity.com/paper/2208.01785/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 12 references that are in the map.
- First posted on arXiv 2 Aug 2022.

## Builds on (most cited first, 5 of 5)

- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [Learning Optimal Resource Allocations in Wireless Systems](https://mlsanity.com/paper/1807.08088/) (2018)
- [Towards Smart and Reconfigurable Environment: Intelligent Reflecting Surface Aided Wireless Network](https://mlsanity.com/paper/1905.00152/) (2019)
- [Software Defined Space-Air-Ground Integrated Vehicular Networks: Challenges and Solutions](https://mlsanity.com/paper/1703.02664/) (2017)
- [Deep Reinforcement Learning for Delay-Oriented IoT Task Scheduling in Space-Air-Ground Integrated Network](https://mlsanity.com/paper/2010.01471/) (2020)

## Built on by (most cited first, 2 of 2)

- [SigT: An Efficient End-to-End MIMO-OFDM Receiver Framework Based on Transformer](https://mlsanity.com/paper/2211.09712/) (2022)
- [Digital Twin-Assisted Knowledge Distillation Framework for Heterogeneous Federated Learning](https://mlsanity.com/paper/2303.06155/) (2023)

## Abstract (arXiv)

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.