LAKER learns a data-dependent preconditioner to reduce condition numbers by up to three orders of magnitude and accelerate convergence over twenty-fold for regularized attention kernel regression in spectrum cartography.
Radio map prediction from aerial images and application to coverage optimization
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Neural models predict coverage- and power-optimal transmitter locations from building maps, matching exhaustive search performance at 14-2400x speedups while quantifying an asymmetric coverage-power trade-off.
A learned model predicts elevation from satellite images to improve radio environment map estimation by up to 7.8% RMSE over image-only methods while eliminating LiDAR requirements during operation.
A dual-branch cross-attention neural network with recurrent tracking reconstructs complete channel impulse responses from satellite imagery by predicting TDL parameters, reaching over 0.96 PDP cosine similarity on unseen sites.
The paper overviews attention-based learning methods for spectrum cartography in LEO satellite networks to enable adaptive fusion of heterogeneous measurements for inference and resource allocation.
citing papers explorer
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Accelerating Regularized Attention Kernel Regression for Spectrum Cartography
LAKER learns a data-dependent preconditioner to reduce condition numbers by up to three orders of magnitude and accelerate convergence over twenty-fold for regularized attention kernel regression in spectrum cartography.
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Learning Coverage- and Power-Optimal Transmitter Placement from Building Maps: A Comparative Study of Direct and Indirect Neural Approaches
Neural models predict coverage- and power-optimal transmitter locations from building maps, matching exhaustive search performance at 14-2400x speedups while quantifying an asymmetric coverage-power trade-off.
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Learned Elevation Models as a Lightweight Alternative to LiDAR for Radio Environment Map Estimation
A learned model predicts elevation from satellite images to improve radio environment map estimation by up to 7.8% RMSE over image-only methods while eliminating LiDAR requirements during operation.
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Deep Learning-Based Site-Specific Channel Modeling and Inference
A dual-branch cross-attention neural network with recurrent tracking reconstructs complete channel impulse responses from satellite imagery by predicting TDL parameters, reaching over 0.96 PDP cosine similarity on unseen sites.
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Learning-Based Spectrum Cartography in Low Earth Orbit Satellite Networks: An Overview
The paper overviews attention-based learning methods for spectrum cartography in LEO satellite networks to enable adaptive fusion of heterogeneous measurements for inference and resource allocation.