Scalability of Graph Neural Network Policies in Wireless Communication Networks
A theoretical framework for GNN transferability over sparse Random Geometric Graphs (RGGs) is established, capturing distance-dependent channel decay and spatial interference and extending this framework to conflict graph models, deriving equivalent scalability guarantees for link-level resource allocation policies.
Romina García Camargo, Zhiyang Wang, Alejandro Ribeiro
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