Hybrid-Task Meta-Learning: A Graph Neural Network Approach for Scalable and Transferable Bandwidth Allocation

• URL: http://arxiv.org/abs/2401.10253v2
• Date: Mon, 18 Mar 2024 03:01:08 GMT
• Title: Hybrid-Task Meta-Learning: A Graph Neural Network Approach for Scalable and Transferable Bandwidth Allocation
• Authors: Xin Hao, Changyang She, Phee Lep Yeoh, Yuhong Liu, Branka Vucetic, Yonghui Li,
• Abstract summary: We develop a deep learning-based bandwidth allocation policy that is scalable with the number of users and transferable to different communication scenarios. To support scalability, the bandwidth allocation policy is represented by a graph neural network (GNN) We develop a hybrid-task meta-learning algorithm that trains the initial parameters of the GNN with different communication scenarios.
• Score: 46.342827102556896
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 sampling 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.

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