Spectrum Cartography
3 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Spectrum Cartography
Latest papers
Quantized Radio Map Estimation Using Tensor and Deep Generative Models
XiaoFuLab/Quantized-Radio-Map-Estimation-BTD-and-DGM
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Spectrum cartography (SC), also known as radio map estimation (RME), aims at crafting multi-domain (e. g., frequency and space) radio power propagation maps from limited sensor measurements.
Deep Spectrum Cartography: Completing Radio Map Tensors Using Learned Neural Models
However, such deep learning (DL)-based SC approaches encounter serious challenges in both off-line model learning (training) and completion (generalization), possibly because the latent state space for generating the radio maps is prohibitively large.
Location-free Spectrum Cartography
Spectrum cartography constructs maps of metrics such as channel gain or received signal power across a geographic area of interest using spatially distributed sensor measurements.
