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Modular Meta-Learning for Power Control via Random Edge Graph Neural Networks. (arXiv:2108.13178v2 [cs.NI] CROSS LISTED)
cs.LG updates on arXiv.org arxiv.org
In this paper, we consider the problem of power control for a wireless
network with an arbitrarily time-varying topology, including the possible
addition or removal of nodes. A data-driven design methodology that leverages
graph neural networks (GNNs) is adopted in order to efficiently parametrize the
power control policy mapping the channel state information (CSI) to transmit
powers. The specific GNN architecture, known as random edge GNN (REGNN),
defines a non-linear graph convolutional filter whose spatial weights are tied
to the …
arxiv edge graph graph neural networks learning meta meta-learning modular networks neural networks power random