Learning Decentralized Wireless Resource Allocations with Graph Neural Networks

Abstract

We consider the broad class of decentralized optimal resource allocation problems in wireless networks, which can be formulated as a constrained statistical learning problems with a localized information structure. We develop the use of Aggregation Graph Neural Networks (Agg-GNNs), which process a sequence of delayed and potentially asynchronous graph aggregated state information obtained locally at each transmitter from multi-hop neighbors. We further utilize model-free primal-dual learning methods to optimize performance subject to constraints in the presence of delay and asynchrony inherent to decentralized networks. We demonstrate a permutation equivariance property of the resulting resource allocation policy that can be shown to facilitate transference to dynamic network configurations. The proposed framework is validated with numerical simulations that exhibit superior performance to baseline strategies.

arXiv comment13 pages, 13 figures

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• AI-based Fog and Edge Computing: A Systematic Review, Taxonomy and Future Directions
• Learning Resilient Radio Resource Management Policies with Graph Neural Networks
• State-Augmented Learnable Algorithms for Resource Management in Wireless Networks
• An Overview on the Application of Graph Neural Networks in Wireless Networks
• Graph Neural Networks for Power Allocation in Wireless Networks with Full Duplex Nodes
• Accelerating Graph Neural Networks via Edge Pruning for Power Allocation in Wireless Networks
• Asynchronous Message-Passing and Zeroth-Order Optimization Based Distributed Learning with a Use-Case in Resource Allocation in Communication Networks
• A Deep Learning Based Resource Allocator for Communication Networks with Dynamic User Utility Demands