Dual score-map neural models match exhaustive balanced transmitter placement (d̄=2.60) at 14–22× speedups on a 167k-scenario urban dataset, outperforming heatmap baselines overall.
Indoor radio map dataset
2 Pith papers cite this work, alongside 5 external citations. Polarity classification is still indexing.
2
Pith papers citing it
5
external citations · OpenAlex
verdicts
UNVERDICTED 2representative citing papers
A unified deep learning model predicts FR3 signal strength from FR1 data and sparse measurements to cut simulation and measurement costs in 6G networks.
citing papers explorer
-
Learning Coverage- and Power-Optimal Transmitter Placement from City Maps: A Comparative Study of Direct and Indirect Neural Approaches
Dual score-map neural models match exhaustive balanced transmitter placement (d̄=2.60) at 14–22× speedups on a 167k-scenario urban dataset, outperforming heatmap baselines overall.
-
CommUNext: Deep Learning-Based Cross-Band and Multi-Directional Signal Prediction
A unified deep learning model predicts FR3 signal strength from FR1 data and sparse measurements to cut simulation and measurement costs in 6G networks.