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This paper formulates indoor radio map estimation as a collaborative in-context learning problem and proposes Poly-Ripple, a framework where multiple clients use large autoregressive vision models without model updates, employing ripple-ordered autoregressive modeling and importance-aware aggregation to outperform ICL baselines.
Proposes EA-RMENet, a deep learning model for radio map estimation that achieves high accuracy and efficiency using an EfficientNetB5 encoder, attention gated skip connections, and ASPP.