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.
Spatial transformers for radio map estima- tion
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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.
A tutorial organizes learning-based radio map construction around data sources, neural architectures, and physics-awareness integration for wireless environments.
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-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.
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A Tutorial on Learning-Based Radio Map Construction: Data, Paradigms, and Physics-Awareness
A tutorial organizes learning-based radio map construction around data sources, neural architectures, and physics-awareness integration for wireless environments.