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，一切都将被连接起来。 在高度拥挤、自组织和异构的无线通信环境中，先进的射频（RF）感知技术将成为无线资源管理、干扰避免、传输优化和决策制定的基石。为了提高RF感知，频谱制图制作了跨多个维度的“无线电地图”（例如，时间、频率和空间）。现有方法通常依赖于过度简化的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。利用人工智能的最新进展，该项目旨在利用有限的（即，几位的）信息交换，这在很大程度上是未知的研究沃茨。特别是，该项目旨在设计一类潜在神经因子分析（LaNFAC）模型，以简约的方式表示RF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RF环境。本计画所发展之理论与方法可广泛应用于地球科学、食品科学、影像处理、医学影像等领域。该研究将加强本科教育，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，旨在提高他们在信号和机器智能方面的职业前景。该项目将开发一套分析和计算工具，用于从少量测量位，通过开发各种用于无线电地图建模的LaNFAC工具。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。这项工作将首先在现实的RF环境中开发基于有限反馈和LaNFAC-assisted频谱制图的基本框架。然后，该项目将考虑更具挑战性的场景（例如，没有训练数据），并使用未经训练的LaNFAC模型从量化信息反馈/交换中开发可证明的光谱制图。最后的研究推力将验证理论和评估算法使用精心设计的模拟器和软件定义的无线电实验使用真实的数据。使用未经训练的神经模型保留了强大的表现力，而不依赖于训练数据，这将促进分布式，交换限制和自适应频谱制图。真实数据的获取和发布将帮助研究界开发有效的和可重复的频谱制图方法，并最终以集体的方式推进对RF意识问题的理解。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

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.