A Generative Flow Network framework with experience replay, exploratory policy, and physics masking samples ray paths for radio propagation up to 100x faster than exhaustive search on idealized scenarios.
Machine Learning for Wireless Communication Channel Modeling: An Overview,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
A systematic literature survey that classifies data-driven KPI prediction methods for 6G networks across KPI type, data source, protocol stack layer, horizon, model family, and objective.
citing papers explorer
-
Transform-Invariant Generative Ray Path Sampling for Efficient Radio Propagation Modeling
A Generative Flow Network framework with experience replay, exploratory policy, and physics masking samples ray paths for radio propagation up to 100x faster than exhaustive search on idealized scenarios.
-
AI-Based KPI Prediction Methods in Future 6G Networks: A Survey
A systematic literature survey that classifies data-driven KPI prediction methods for 6G networks across KPI type, data source, protocol stack layer, horizon, model family, and objective.