Spectrum cartography (SC), also known as radio map estimation (RME), aims at crafting multi-domain (e.g., frequency and space) radio power propagation maps from limited sensor measurements. While early methods often lacked theoretical support, recent works have demonstrated that radio maps can be provably recovered using low-dimensional models -- such as the block-term tensor decomposition (BTD) model and certain deep generative models (DGMs) -- of the high-dimensional multi-domain radio signals. However, these existing provable SC approaches assume that sensors send real-valued (full-resolution) measurements to the fusion center, which is unrealistic. This work puts forth a quantized SC framework that generalizes the BTD and DGM-based SC to scenarios where heavily quantized sensor measurements are used. A maximum likelihood estimation (MLE)-based SC framework under a Gaussian quantizer is proposed. Recoverability of the radio map using the MLE criterion are characterized under realistic conditions, e.g., imperfect radio map modeling and noisy measurements. Simulations and real-data experiments are used to showcase the effectiveness of the proposed approach.

翻译：频谱绘图（SC），也称为无线电地图估计（RME），旨在利用有限的传感器测量值构建多域（如频率与空间）无线电功率传播图。尽管早期方法常缺乏理论支撑，但近期研究表明，通过利用高维多域无线电信号的低维模型（如分块张量分解（BTD）模型和特定深度生成模型（DGM）），可证明地恢复无线电地图。然而，现有可证明的SC方法假设传感器向融合中心发送实值（全分辨率）测量值，这在实际中并不现实。本文提出一种量化SC框架，将基于BTD和DGM的SC方法推广至使用重度量化传感器测量值的场景。在此框架下，基于高斯量化器提出一种最大似然估计（MLE）的SC方法。在现实条件（如无线电地图建模不完善和测量噪声）下，刻画了采用MLE准则恢复无线电地图的可恢复性。通过仿真实验和真实数据实验验证了所提方法的有效性。