Radio Environment Maps (REMs) have the potential to serve as an important enabler for intelligent modeling and control in emerging AI-native 6G networks. Despite significant progress, most REM construction methods remain passive, relying on interpolation or static uncertainty models and lacking an explicit mechanism to reason about how future measurements will affect reconstruction quality under a limited measurement budget. In this paper, we formulate REM construction as a sequential decision-making problem and propose a world-model-inspired framework for active Received Signal Strength Indicator (RSSI) map reconstruction. By learning an internal representation of the radio environment and employing a dreaming mechanism to simulate the impact of candidate measurements, the proposed approach actively selects measurement locations under a limited budget. Experimental results on real indoor RSSI data demonstrate that the proposed method significantly outperforms Gaussian Process-based interpolation in the few-shot regime, achieving up to a fivefold reduction in Root Mean Square Error (RMSE) with the same number of measurements. These results highlight the potential of world models as a powerful paradigm for sample-efficient radio environment mapping and intelligent model-based sensing in 6G and beyond networks.

翻译：无线电环境地图（REMs）有望成为新兴的AI原生6G网络中智能建模与控制的重要使能技术。尽管已有显著进展，但大多数REM构建方法仍是被动的，依赖于插值或静态不确定性模型，缺乏明确机制来推理在有限测量预算下未来测量如何影响重建质量。本文将REM构建问题形式化为序列决策问题，并提出一种基于世界模型的主动接收信号强度指示（RSSI）地图重建框架。通过学习无线电环境的内部表征，并采用"梦想"机制模拟候选测量的影响，所提方法在有限预算下主动选择测量位置。基于真实室内RSSI数据的实验表明，在少样本场景下，所提方法显著优于基于高斯过程的插值方法，在相同测量次数下实现了高达五倍的均方根误差（RMSE）降低。这些结果凸显了世界模型作为高效样本的无线电环境地图构建范式以及6G及未来网络中基于模型的智能感知的强大潜力。