2024
DOI: 10.1109/ojsp.2024.3378591
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REM-U-Net: Deep Learning Based Agile REM Prediction With Energy-Efficient Cell-Free Use Case

Hazem Sallouha,
Shamik Sarkar,
Enes Krijestorac
et al.

Abstract: Radio environment maps (REMs) hold a central role in optimizing wireless network deployment, enhancing network performance, and ensuring effective spectrum management. Conventional REM prediction methods are either excessively time-consuming, e.g., ray tracing, or inaccurate, e.g., statistical models, limiting their adoption in modern inherently dynamic wireless networks. Deep learningbased REM prediction has recently attracted considerable attention as an appealing, accurate, and timeefficient alternative. Ho… Show more

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Cited by 4 publications
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