IGCNet learns power control policies for interference channels via graph convolutions, is proven to be a universal approximator for permutation-invariant continuous functions, and outperforms WMMSE in speed while remaining robust to imperfect CSI.
Spatial deep learning for wireless scheduling,
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A Graph Neural Network Approach for Scalable Wireless Power Control
IGCNet learns power control policies for interference channels via graph convolutions, is proven to be a universal approximator for permutation-invariant continuous functions, and outperforms WMMSE in speed while remaining robust to imperfect CSI.