Spectrum cartography aims at estimating power propagation patterns over a geographical region across multiple frequency bands (i.e., a radio map)—from limited samples taken sparsely over the region. Classic cartography methods are mostly concerned with recovering the aggregate radio frequency (RF) information while ignoring the constituents of the radio map—but fine-grained emitter-level RF information is of great interest. In addition, many existing cartography methods explicitly or implicitly assume random spatial sampling schemes that may be difficult to implement, due to legal/privacy/security issues. The theoretical aspects (e.g., identifiability of the radio map) of many existing methods are also unclear. In this work, we propose a joint radio map recovery and disaggregation method that is based on coupled block-term tensor decomposition. Our method guarantees identifiability of the individual radio map of each emitter (thereby that of the aggregate radio map as well), under realistic conditions. The identifiability result holds under a large variety of geographical sampling patterns, including a number of pragmatic systematic sampling strategies. We also propose effective optimization algorithms to carry out the formulated radio map disaggregation problems. Extensive simulations are employed to showcase the effectiveness of the proposed approach.

中文翻译：

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通过耦合块项张量分解的频谱制图

频谱制图旨在通过在该地区稀疏采集的有限样本估计跨多个频带（即无线电地图）在地理区域上的功率传播模式。经典的制图方法主要关注恢复综合射频 (RF) 信息，同时忽略无线电地图的组成部分——但细粒度的发射器级 RF 信息非常有趣。此外，由于法律/隐私/安全问题，许多现有的制图方法明确或隐含地假设可能难以实施的随机空间采样方案。许多现有方法的理论方面（例如，无线电地图的可识别性）也不清楚。在这项工作中，我们提出了一种基于耦合块项张量分解的联合射电图恢复和分解方法。我们的方法保证了在现实条件下每个发射器的单个无线电地图的可识别性（因此也是聚合无线电地图的可识别性）。可识别性结果适用于多种地理抽样模式，包括许多实用的系统抽样策略。我们还提出了有效的优化算法来执行制定的无线电地图分解问题。大量的模拟被用来展示所提出方法的有效性。我们还提出了有效的优化算法来执行制定的无线电地图分解问题。大量的模拟被用来展示所提出方法的有效性。我们还提出了有效的优化算法来执行制定的无线电地图分解问题。大量的模拟被用来展示所提出方法的有效性。