Rethinking Radiomap Blind Prediction with Limited Environment and Configuration Representations

2026-09-10

AI Summary

本文解决了无线电图盲预测问题，通过使用环境和基站配置的有限表示，提出RadioDecomp方法来减少预测误差和不确定性。

Abstract

Radiomap blind prediction infers radiomaps from observable representations of the propagation environment and base station (BS) configuration without field measurements. These representations are inherently incomplete and cannot uniquely determine the target radiomap. Under squared loss, we identify the conditional-mean radiomap as the population-optimal deterministic target and decompose domain risk into target-approximation error and irreducible uncertainty. The train-test risk gap motivates propagation priors as cross-domain guidance, although their partial or simplified forms may bias the attainable predictor. We therefore propose RadioDecomp, which treats a prior-guided predictor as a correctable base and uses deterministic residual refinement to learn its remaining predictable discrepancy. We instantiate RadioDecomp as RadioLSR (LoS-Shadow-Residual). Experiments under cross-configuration and cross-environment settings show that RadioLSR is especially effective for cross-configuration generalization and provides overall gains over a controlled monolithic counterpart under cross-environment generalization.

Problem

• Radiomap blind prediction
• Propagation environment
• Base station configuration
• Uncertainty

Innovation

• Radiomap blind prediction
• Propagation priors
• Deterministic residual refinement
• Cross-configuration generalization
• Cross-environment generalization