Graph Representation Learning for Wireless Communications
Mohsenivatani, Maryam; Ali, Samad; Ranasinghe, Vismika; Rajatheva, Nandana; Latva-Aho, Matti (2023-04-17)
IEEE
17.04.2023
M. Mohsenivatani, S. Ali, V. Ranasinghe, N. Rajatheva and M. Latva-Aho, "Graph Representation Learning for Wireless Communications," in IEEE Communications Magazine, vol. 62, no. 1, pp. 141-147, January 2024, doi: 10.1109/MCOM.001.2200810.
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© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202503262209
https://urn.fi/URN:NBN:fi:oulu-202503262209
Tiivistelmä
Abstract
Wireless networks are inherently graph-structured in which graph representation learning can be utilized to solve complex network optimization problems. In graph representation learning, feature vectors for each entity in the network are calculated such that they could capture spatial and temporal dependencies in their local and global neighborhoods. Specifically, graph neural networks (GNNs) are powerful tools to solve these complex problems because of their expressive representation and reasoning power. In this article, the potential of graph representation learning and GNNs in wireless networks is presented. An overview of graph representation learning is provided which covers the fundamentals and concepts, such as feature design over graphs, GNNs, and their design principles. The potential of graph representation learning in wireless networks is presented via a few exemplary use cases and some initial results on the GNN-based access point selection for cell-free massive Multiple-Input Multiple-Output (MIMO) systems.
Wireless networks are inherently graph-structured in which graph representation learning can be utilized to solve complex network optimization problems. In graph representation learning, feature vectors for each entity in the network are calculated such that they could capture spatial and temporal dependencies in their local and global neighborhoods. Specifically, graph neural networks (GNNs) are powerful tools to solve these complex problems because of their expressive representation and reasoning power. In this article, the potential of graph representation learning and GNNs in wireless networks is presented. An overview of graph representation learning is provided which covers the fundamentals and concepts, such as feature design over graphs, GNNs, and their design principles. The potential of graph representation learning in wireless networks is presented via a few exemplary use cases and some initial results on the GNN-based access point selection for cell-free massive Multiple-Input Multiple-Output (MIMO) systems.
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