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 noisy environment information and sparse observations
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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.
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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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.