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

arXiv:2401.10253v2 Announce Type: replace-cross
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 …

abstract arxiv bandwidth channels communication cs.lg cs.ni deep learning graph graph neural network hybrid meta meta-learning network neural network paper policy quality requirements resources scalable service support type wireless