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.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Develops an alternative minimization framework with a DC programming algorithm to solve the non-convex transmit power minimization problem in IRS-empowered NOMA networks.
citing papers explorer
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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.
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Intelligent Reflecting Surface for Downlink Non-Orthogonal Multiple Access Networks
Develops an alternative minimization framework with a DC programming algorithm to solve the non-convex transmit power minimization problem in IRS-empowered NOMA networks.