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

TL;DR

This paper introduces a scalable and transferable bandwidth allocation method using a graph neural network trained with hybrid-task meta-learning, enabling efficient adaptation to various communication scenarios with significant performance gains.

Contribution

The paper proposes a novel hybrid-task meta-learning algorithm for GNNs, enhancing scalability and transferability in bandwidth allocation across diverse communication environments.

Findings

Improves initial performance by 8.79%.

Enhances sample efficiency by 73%.

Reduces computation time by 200-2000 times.

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 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.