CIF: Small: Latent Neural Factor Models for Radio Cartography From Bits

在下一代智能、认知和软件定义的无线系统中，一切都有望实现真正的连接。在高度拥挤、自组织和异质的无线通信环境中，先进的射频(RF)感知技术将成为无线资源管理、干扰避免、传输优化和决策的基石。为了提高射频感知能力，频谱制图从有限的传感器和测量数据中绘制出跨越多个维度(例如，时间、频率和空间)的“无线电地图”。现有方法通常依赖过于简化的RF环境模型(例如，平滑和静态无线电地图)和问题设置(例如，使用未量化的开销)，这在应用于真实世界设置时降低了性能。利用人工智能领域的最新进展，该项目旨在利用有限的(即，几个比特)信息交换，在复杂、重度阴影和动态环境下开发光谱制图理论和方法，这些信息交换在很大程度上是未知的研究水域。特别是，该项目试图设计一类潜在神经因素分析(LaNFAC)模型来以简明的方式表示射频环境。利用LaNFAC模型，该项目将提供频谱制图方法，从有限和量化的测量中重建真实的射频环境。本项目开发的理论和方法在地学、食品科学、视频处理和医学成像等学科中具有广泛的应用前景。这项研究将支持本科教育，并为来自代表性不足和服务不足群体的学生提供优化、深度学习、张量分析和传感方面的培训机会，以期提高他们在信号和机器智能领域的职业前景。该项目将通过开发各种用于无线电地图建模的LaNFAC工具，开发一套分析和计算工具，用于从少量测量比特进行可证明、稳健和有效的频谱制图。LNFAC模型是潜在因素分析模型(例如张量分解)和神经生成模型的明智结合。这项工作将首先在现实的射频环境中开发基于有限反馈和LaNFAC辅助的频谱制图的基本框架。然后，该项目将考虑更具挑战性的情景(例如，没有培训数据)，并利用未经训练的拉纳菲克模型，从量化的信息反馈/交流中开发可证明的频谱制图。最后的研究重点将使用精心设计的模拟器和使用真实数据的软件定义的无线电实验来验证理论和评估算法。使用未经训练的神经模型可以在不依赖训练数据的情况下保持强大的表现力，这将促进分布式、交换受限和自适应的光谱制图。真实数据的获取和发布将帮助研究社区开发有效和可重现的频谱制图方法，并最终以集体的方式促进对射频意识问题的理解。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

In the next generation of intelligent, cognitive and software-defined wireless systems, everything is expected to be connected, literally. Advanced radio frequency (RF) awareness techniques will be the cornerstone of wireless resource management, interference avoidance, transmission optimization, and decision making in a highly crowded, self-organized, and heterogeneous wireless communication environment. To advance RF awareness, spectrum cartography crafts a "radio map" across multiple dimensions (e.g., time, frequency and space) from limited sensors and measurements. Prior approaches often rely on over-simplified RF environment models (e.g., smooth and static radio maps) and problem settings (e.g., using unquantized overhead), which lowers performance when applied in real-world settings. Leveraging recent advances in artificial intelligence, this project aims to develop spectrum cartography theory and methods under complex, heavily shadowed and dynamic environments using limited (i.e., a few bits of) information exchange, which are largely uncharted research waters. In particular, the project seeks to design a class of latent neural factor analysis (LaNFAC) models to represent the RF environments in a parsimonious way. Using the LaNFAC models, the project will offer spectrum cartography approaches to reconstruct realistic RF environments from limited and quantized measurements. Theory and methods developed in this project may find wide application in such disciplines as geoscience, food science, video processing, and medical imaging. The research will bolster undergraduate education and offer training opportunities in optimization, deep learning, tensor analysis, and sensing to students from under-represented and under-served groups with the aim to enhance their career prospects in signal and machine intelligence.This project will develop a suite of analytical and computational tools for provable, robust and efficient spectrum cartography from a small number of measurement bits, by way of developing a variety of LaNFAC tools for radio map modeling. The LNFAC models are a judicious integration of latent factor analysis models (e.g., tensor decomposition) and neural generative models. The work will first develop the basic framework of limited feedback-based and LaNFAC-assisted spectrum cartography in realistic RF environments. Then, the project will consider more challenging scenarios (e.g., no training data) and develop provable spectrum cartography from quantized information feedback/exchange using untrained LaNFAC models. The last research thrust will validate the theory and evaluate the algorithms using carefully designed simulators and software-defined radio experiments using real data. Using untrained neural models retains strong expressiveness without relying on training data, which will facilitate distributed, exchange-limited, and adaptive spectrum cartography. Real-data acquisition and releasing will assist the research community to develop effective and reproducible spectrum cartography approaches, and ultimately advance understanding of the RF awareness problem in a collective way.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.