DOCKING uses ordinary kriging on sparse RSRP/SINR data to reconstruct REMs, approximates dominant attenuation as cuboids, and optimizes MIAB placement via genetic algorithm in smart-port settings.
3D spec- trum awareness for radio dynamic zones using Kriging and ma- trix completion,
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
An ensemble ML framework combining lidar simulation data, SMOTE, and real measurements reduces path loss prediction MAE by up to 50% versus real-data-only models and improves cross-environment generalization.
citing papers explorer
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Mobile Base Station Positioning in Smart Ports Based on Kriged Sparse Measurements and Obstacle Inference
DOCKING uses ordinary kriging on sparse RSRP/SINR data to reconstruct REMs, approximates dominant attenuation as cuboids, and optimizes MIAB placement via genetic algorithm in smart-port settings.
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Simulation-Driven Ensemble Machine Learning for Robust and Generalizable Path Loss Prediction
An ensemble ML framework combining lidar simulation data, SMOTE, and real measurements reduces path loss prediction MAE by up to 50% versus real-data-only models and improves cross-environment generalization.