# Scalable Resource Management for Dynamic MEC: An Unsupervised Link-Output Graph Neural Network Approach

arXiv: https://arxiv.org/abs/2306.08938 · HTML: https://mlsanity.com/paper/2306.08938/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 15 references that are in the map.
- First posted on arXiv 15 Jun 2023.

## Builds on (most cited first, 7 of 7)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Convergence of Edge Computing and Deep Learning: A Comprehensive Survey](https://mlsanity.com/paper/1907.08349/) (2019)
- [Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysis](https://mlsanity.com/paper/2007.07632/) (2020)
- [Towards Optimal Power Control via Ensembling Deep Neural Networks](https://mlsanity.com/paper/1807.10025/) (2018)
- [Toward Immersive Communications in 6G](https://mlsanity.com/paper/2303.05283/) (2023)
- [Digital Twin-Assisted Efficient Reinforcement Learning for Edge Task Scheduling](https://mlsanity.com/paper/2208.01781/) (2022)

## Built on by (most cited first, 5 of 5)

- [RadioDiff: An Effective Generative Diffusion Model for Sampling-Free Dynamic Radio Map Construction](https://mlsanity.com/paper/2408.08593/) (2024)
- [Reliable Projection Based Unsupervised Learning for Semi-Definite QCQP with Application of Beamforming Optimization](https://mlsanity.com/paper/2407.03668/) (2024)
- [Distilling Knowledge from Resource Management Algorithms to Neural Networks: A Unified Training Assistance Approach](https://mlsanity.com/paper/2308.07511/) (2023)
- [Label-free Deep Learning Driven Secure Access Selection in Space-Air-Ground Integrated Networks](https://mlsanity.com/paper/2308.14348/) (2023)
- [GNN-Empowered Effective Partial Observation MARL Method for AoI Management in Multi-UAV Network](https://mlsanity.com/paper/2409.00036/) (2024)

## Abstract (arXiv)

Deep learning has been successfully adopted in mobile edge computing (MEC) to optimize task offloading and resource allocation. However, the dynamics of edge networks raise two challenges in neural network (NN)-based optimization methods: low scalability and high training costs. Although conventional node-output graph neural networks (GNN) can extract features of edge nodes when the network scales, they fail to handle a new scalability issue whereas the dimension of the decision space may change as the network scales. To address the issue, in this paper, a novel link-output GNN (LOGNN)-based resource management approach is proposed to flexibly optimize the resource allocation in MEC for an arbitrary number of edge nodes with extremely low algorithm inference delay. Moreover, a label-free unsupervised method is applied to train the LOGNN efficiently, where the gradient of edge tasks processing delay with respect to the LOGNN parameters is derived explicitly. In addition, a theoretical analysis of the scalability of the node-output GNN and link-output GNN is performed. Simulation results show that the proposed LOGNN can efficiently optimize the MEC resource allocation problem in a scalable way, with an arbitrary number of servers and users. In addition, the proposed unsupervised training method has better convergence performance and speed than supervised learning and reinforcement learning-based training methods. The code is available at urlhttps://github.com/UNIC-Lab/LOGNN.