Papers Spectrum Cartography
“Spectrum Cartography” 태그가 달린 논문 12편 · 필터 해제
Temporal Spectrum Cartography in Low-Altitude Economy Networks: A Generative AI Framework with Multi-Agent Learning
This paper introduces a two-stage generative AI (GenAI) framework tailored for temporal spectrum cartography in low-altitude economy networks (LAENets). LAENets, characterized by diverse aerial devices such as UAVs, rely…
Spectrum CartographyRadio Map Estimation via Latent Domain Plug-and-Play Denoising
Radio map estimation (RME), also known as spectrum cartography, aims to reconstruct the strength of radio interference across different domains (e.g., space and frequency) from sparsely sampled measurements. To tackle th…
Computational EfficiencyDenoisingSpectrum CartographyDomain-Factored Untrained Deep Prior for Spectrum Cartography
Spectrum cartography (SC) focuses on estimating the radio power propagation map of multiple emitters across space and frequency using limited sensor measurements. Recent advances in SC have shown that leveraging learned …
Spectrum CartographyGLIP: Electromagnetic Field Exposure Map Completion by Deep Generative Networks
In Spectrum cartography (SC), the generation of exposure maps for radio frequency electromagnetic fields (RF-EMF) spans dimensions of frequency, space, and time, which relies on a sparse collection of sensor data, posing…
Spectrum CartographyQuantized Radio Map Estimation Using Tensor and Deep Generative Models
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. While early methods often lack…
Spectrum CartographyTensor DecompositionRadio Map Estimation: A Data-Driven Approach to Spectrum Cartography
Radio maps characterize quantities of interest in radio communication environments, such as the received signal strength and channel attenuation, at every point of a geographical region. Radio map estimation typically en…
regressionSpectrum CartographyDeep Spectrum Cartography: Completing Radio Map Tensors Using Learned Neural Models
The spectrum cartography (SC) technique constructs multi-domain (e.g., frequency, space, and time) radio frequency (RF) maps from limited measurements, which can be viewed as an ill-posed tensor completion problem. Model…
Spectrum CartographySpectrum Cartography via Coupled Block-Term Tensor Decomposition
Spectrum cartography aims at estimating power propagation patterns over a geographical region across multiple frequency bands (i.e., a radio map)---from limited samples taken sparsely over the region. Classic cartography…
Spectrum CartographyTensor DecompositionData-Driven Spectrum Cartography via Deep Completion Autoencoders
Spectrum maps, which provide RF spectrum metrics such as power spectral density for every location in a geographic area, find numerous applications in wireless communications such as interference control, spectrum manage…
ManagementSpectrum CartographyLocation-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. Applications of these maps include netwo…
Spectrum CartographyFunctional Nonlinear Sparse Models
Signal processing is rich in inherently continuous and often nonlinear applications, such as spectral estimation, optical imaging, and super-resolution microscopy, in which sparsity plays a key role in obtaining state-of…
Robust classificationSpectrum CartographySuper-ResolutionDecentralized learning for wireless communications and networking
This chapter deals with decentralized learning algorithms for in-network processing of graph-valued data. A generic learning problem is formulated and recast into a separable form, which is iteratively minimized using th…
Spectrum CartographyState Estimation