不完整观测下的无线电图盲预测：误差表征与可修正传播先验学习
Radiomap Blind Prediction under Incomplete Observation: Error Characterization and Correctable Propagation-Prior Learning
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中文总结 AI 辅助
针对不完整观测下的无线电图盲预测，提出理论框架分解误差并揭示传播先验的双重作用，进而设计RadioDecomp通过残差细化修正先验引导预测器，实验验证其优于各基础模型。
中文摘要 AI 辅助
无线电图盲预测旨在无需现场测量，仅从传播环境的可观测表示和基站配置中推断无线电图。在实际中，可观测表示本质上是不完整的。因此，当泛化到未见配置或环境时，目标无线电图并非由输入完全决定。在不完整观测下，我们建立了确定性无线电图盲预测的总体级理论，该理论将条件均值识别为其最优目标，并将预测误差分解为可减少的预测器近似误差和由缺失物理信息引起的不确定性。该框架进一步刻画了训练-测试风险差距以及观测丰富化所带来的不确定性减少。在此基础上，我们揭示了传播先验的双重作用：它们提供物理依据的指导，但其可实现形式可能使可达到的预测器产生偏差。这促使我们提出RadioDecomp，它将先验引导的预测器视为可修正的基础，并通过残差细化学习其剩余的可预测差异。为了在不同传播先验设计下评估RadioDecomp，我们分别用特征引导的整体基础和LoS-Shadow结构化基础实例化它，产生RadioFR和RadioLSR。在随机、跨配置和跨环境设置中，实验证实了传播相关表示的好处，并表明两种实例化均优于各自的基础。进一步对基础容量、训练支持覆盖率和观测粗化的受控研究证实了所提出的分析。
英文摘要
Radiomap blind prediction aims to infer radiomaps from observable representations of the propagation environment and base station configuration without field measurements. In practice, the observable representations are inherently incomplete. Thus, the target radiomap is not fully determined by the inputs when generalizing to unseen configurations or environments. Under incomplete observation, we establish a population-level theory of deterministic radiomap blind prediction that identifies the conditional mean as its optimal target and separates prediction error into reducible predictor approximation and irreducible uncertainty caused by missing physical information. The framework further characterizes the train-test risk gap and the uncertainty reduction enabled by observation enrichment. Building on it, we reveal the dual role of propagation priors: they provide physically grounded guidance, yet their implementable forms may bias the attainable predictor. This motivates RadioDecomp, which treats a prior-guided predictor as a correctable base and learns its remaining predictable discrepancy through residual refinement. To evaluate RadioDecomp across distinct propagation-prior designs, we instantiate it with a feature-guided monolithic base and a LoS-Shadow structured base, yielding RadioFR and RadioLSR, respectively. Across random, cross-configuration, and cross-environment settings, experiments confirm the benefit of propagation-related representations and show that both instantiations improve upon their respective bases. Further controlled studies on base capacity, training-support coverage, and observation coarsening corroborate the proposed analysis.
发表机构
- Southeast University（东南大学）
- National Mobile Communications Research Laboratory, Southeast University（东南大学移动通信国家重点实验室）
- Nanjing University（南京大学）
- State Key Laboratory of Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）
- Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong-Shenzhen（香港中文大学（深圳）深圳大数据研究院）
- National Sun Yat-sen University（国立中山大学）
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