A deep learning model decomposes and reconstructs spatial correlation maps from sparse samples using attention and multi-scale fusion, reporting cosine similarity above 0.8 on the CKMImageNet dataset.
CKMDiff: A generative diffusion model for CKM construction via inverse problems with learned priors
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A neural network-based differentiable CKM construction method enables joint power-bandwidth-trajectory optimization for multi-UAV systems, achieving higher minimum throughput than statistical channel models.
A new site-specific model uses 3D geometry maps and recursive UTD diffraction calculations to predict urban radio path loss and time-varying Doppler more accurately than 3GPP models, with RMSE reductions of 7.1 dB in complex NLOS cases.
A single channel knowledge map built for communication can be directly reused for NLoS sensing by transforming angle-delay priors via virtual UE modeling, yielding better localization than geometry-based methods in simulations.
Active learning with Bayesian diffusion models selects informative sampling locations to improve channel gain map reconstruction efficiency over baselines on static and dynamic datasets.
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CKM Beyond Channel Gain: Spatial Correlation Map Construction with Deep Learning
A deep learning model decomposes and reconstructs spatial correlation maps from sparse samples using attention and multi-scale fusion, reporting cosine similarity above 0.8 on the CKMImageNet dataset.