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- W4315777680 abstract "In this paper, we investigate Artificial Intelligence (AI)-based beam management (BM) schemes to deal with the beam selection issue with less beam measurement overhead. We design a VGG (Visual Geometry Group) based network, named as VGG-BMNet, to predict the quality of all the beam pairs based on the measured quality of partial beam pairs. To improve the generalization capability of VGG-BMNet, a Unified-VGG-BMNet is proposed, which can achieve higher average beam prediction accuracy than VGG-BMNet. To speed up the training time and reduce the size of training dataset, a transfer learning-based VGG-BMNet is further designed. The simulation results show that transfer learning-based VGG-BMNet not only achieves good generalization capability, but also has much faster convergence rate with fewer training samples. All the proposed schemes can reduce the beam measurement overhead by more than 80% meanwhile achieving comparable beam selection accuracy as the exhaustive beam measurement method." @default.
- W4315777680 created "2023-01-13" @default.
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- W4315777680 date "2022-12-04" @default.
- W4315777680 modified "2023-10-17" @default.
- W4315777680 title "Artificial Intelligence-Based Spatial Domain Beam Prediction for 5G Beyond" @default.
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- W4315777680 doi "https://doi.org/10.1109/gcwkshps56602.2022.10008651" @default.
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