Recursive GNNs for Learning Precoding Policies With Size-Generalizability

Graph neural networks (GNNs) have been shown promising in optimizing power allocation and link scheduling with good size generalizability and low training complexity. These merits are important for learning wireless policies under dynamic environments, which partially come from the matched permutati...

Täydet tiedot

Bibliografiset tiedot

Päätekijät:	Jia Guo, Chenyang Yang
Aineistotyyppi:	Artigo
Kieli:	Inglês
Julkaistu:	IEEE 2024-01-01
Sarja:	IEEE Transactions on Machine Learning in Communications and Networking
Aiheet:	Graph neural networks size generalizability precoding permutation equivariance
Linkit:	https://ieeexplore.ieee.org/document/10716720/
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