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• W4386075259 abstract "The state-of-the-art coding schemes for topological interference management (TIM) problems are usually handcrafted for specific families of network topologies, relying critically on experts' domain knowledge. This inevitably restricts the potential wider applications to wireless communication systems, due to the limited generalizability. This work makes the first attempt to advocate a novel intelligent coding approach to mimic topological interference alignment via local graph coloring algorithms, leveraging the new advances of graph neural networks (GNNs) and reinforcement learning (RL). The extensive experiments demonstrate the excellent generalizability and transferability of the proposed approach, where the parameterized GNNs trained by small size TIM instances are able to work well on new unseen network topologies with larger size." @default.
• W4386075259 created "2023-08-23" @default.
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• W4386075259 date "2023-06-25" @default.
• W4386075259 modified "2023-09-27" @default.
• W4386075259 title "Learning to Code on Graphs for Topological Interference Management" @default.
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• W4386075259 doi "https://doi.org/10.1109/isit54713.2023.10206636" @default.
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