  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.
