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.
Sparse-guided RadioUNet with adaptive sampling for the MLSP 2025 sampling-assisted pathloss radio map prediction data competition,
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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.