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- W3010475835 abstract "Over the last decade there has been a large research interest in exploiting terminal positions for various cellular network services and communication aspects. However, the relevance of terminal coordinates for resource allocation is relatively unexplored to date. In this work, we thus take a first step in that direction by studying coordinates-based resource allocation in an arguably favorable, and straightforward set-up. In particular, we consider the usage of supervised machine learning for resource allocation. Our results show that for the studied scenario, coordinates-based resource allocation can achieve a comparable performance to a CSI-based comparison scheme. While the main limiting factors are channel uncertainty as well as the accuracy of the terminal coordinates, in particular more complex machine learning schemes like Random Forests are able to provide some robustness despite the above mentioned noisy features." @default.
- W3010475835 created "2020-03-13" @default.
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- W3010475835 date "2019-12-01" @default.
- W3010475835 modified "2023-10-04" @default.
- W3010475835 title "On the Feasibility of Coordinates-Based Resource Allocation through Machine Learning" @default.
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- W3010475835 doi "https://doi.org/10.1109/globecom38437.2019.9013883" @default.
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