# Hybrid-Task Meta-Learning: A GNN Approach for Scalable and Transferable Bandwidth Allocation

arXiv: https://arxiv.org/abs/2401.10253 · HTML: https://mlsanity.com/paper/2401.10253/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 46 references that are in the map.
- First posted on arXiv 23 Dec 2023.

## Builds on (most cited first, 19 of 19)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [On First-Order Meta-Learning Algorithms](https://mlsanity.com/paper/1803.02999/) (2018)
- [Learning to learn by gradient descent by gradient descent](https://mlsanity.com/paper/1606.04474/) (2016)
- [Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML](https://mlsanity.com/paper/1909.09157/) (2019)
- [Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysis](https://mlsanity.com/paper/2007.07632/) (2020)
- [6G Wireless Communication Systems: Applications, Requirements, Technologies, Challenges, and Research Directions](https://mlsanity.com/paper/1909.11315/) (2019)
- [Deep Reinforcement Learning Based Intelligent Reflecting Surface for Secure Wireless Communications](https://mlsanity.com/paper/2002.12271/) (2020)
- [Intelligent Resource Slicing for eMBB and URLLC Coexistence in 5G and Beyond: A Deep Reinforcement Learning Based Approach](https://mlsanity.com/paper/2003.07651/) (2020)
- [Deep Learning for Radio Resource Allocation with Diverse Quality-of-Service Requirements in 5G](https://mlsanity.com/paper/2004.00507/) (2020)
- [Embedding Model Based Fast Meta Learning for Downlink Beamforming Adaptation](https://mlsanity.com/paper/2109.09086/) (2021)
- [Graph Neural Networks for Distributed Power Allocation in Wireless Networks: Aggregation Over-the-Air](https://mlsanity.com/paper/2207.08498/) (2022)
- [Meta-Reinforcement Learning for Reliable Communication in THz/VLC Wireless VR Networks](https://mlsanity.com/paper/2102.12277/) (2021)
- [Multiservice-based Network Slicing Orchestration with Impatient Tenants](https://mlsanity.com/paper/1909.04585/) (2019)
- [Deep Multi-Task Learning for Cooperative NOMA: System Design and Principles](https://mlsanity.com/paper/2007.13495/) (2020)
- [LACO: A Latency-Driven Network Slicing Orchestration in Beyond-5G Networks](https://mlsanity.com/paper/2009.03771/) (2020)
- [Interference-Limited Ultra-Reliable and Low-Latency Communications: Graph Neural Networks or Stochastic Geometry?](https://mlsanity.com/paper/2207.06918/) (2022)
- [Secure Deep Reinforcement Learning for Dynamic Resource Allocation in Wireless MEC Networks](https://mlsanity.com/paper/2312.08016/) (2023)
- [Graph Neural Network-Based Bandwidth Allocation for Secure Wireless Communications](https://mlsanity.com/paper/2312.14958/) (2023)

## Built on by (most cited first, 1 of 1)

- [BCR-DRL: Behavior- and Context-aware Reward for Deep Reinforcement Learning in Human-AI Coordination](https://mlsanity.com/paper/2408.07877/) (2024)

## Abstract (arXiv)

In this paper, we develop a deep learning-based bandwidth allocation policy that is: 1) scalable with the number of users and 2) transferable to different communication scenarios, such as non-stationary wireless channels, different quality-of-service (QoS) requirements, and dynamically available resources. To support scalability, the bandwidth allocation policy is represented by a graph neural network (GNN), with which the number of training parameters does not change with the number of users. To enable the generalization of the GNN, we develop a hybrid-task meta-learning (HML) algorithm that trains the initial parameters of the GNN with different communication scenarios during meta-training. Next, during meta-testing, a few samples are used to fine-tune the GNN with unseen communication scenarios. Simulation results demonstrate that our HML approach can improve the initial performance by 8.79%, and sample efficiency by 73%, compared with existing benchmarks. After fine-tuning, our near-optimal GNN-based policy can achieve close to the same reward with much lower inference complexity compared to the optimal policy obtained using iterative optimization. Numerical results validate that our HML can reduce the computation time by approximately 200 to 2000 times than the optimal iterative algorithm.