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• W3009073399 abstract "Resource optimization for small-cell wireless networks is more complicated than the traditional applications. The solution needs to be delivered promptly to respond the highly dynamic temporal and spatial variations. It seems that the machine learning strategy is more flexible and adaptive than the conventional optimization methods, since ML has the potential to find the implicit function relationship between arbitrary input data and output results. In this work, we focus on a generic D2D network and to show the effectiveness of ML apply to solve the power optimization problem with different optimization models. The research spans over all stages such as analysis, design, implementation, and validation. It is shown that the ML method has achieved several benchmarks in terms of QoS metrics for different optimization models." @default.
• W3009073399 created "2020-03-13" @default.
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• W3009073399 date "2019-12-01" @default.
• W3009073399 modified "2023-10-18" @default.
• W3009073399 title "Machine Learning for Power Allocation of a D2D Network" @default.
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• W3009073399 doi "https://doi.org/10.1109/globecom38437.2019.9013762" @default.
• W3009073399 hasPublicationYear "2019" @default.
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