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

CIF：小：来自 Bits 的无线电制图的潜在神经因子模型

• 批准号: 2210004
• 负责人: Xiao Fu
• 金额: $ 48.4万
• 依托单位: Oregon State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2210004&HistoricalAwards=false
• 关键词: CIF; Small; Latent; Neural; Factor

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.

在下一代的智能，认知和软件定义的无线系统中，从字面上看，一切都将被预期连接。 高级射频（RF）意识技术将是无线资源管理，避免干扰，传输优化和决策的基石，在高度拥挤，自组织和异质的无线通信环境中。为了提高RF意识，Spectrum制图制作了有限的传感器和测量值跨越多个维度（例如时间，频率和空间）的“无线电图”。先前的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图）和问题设置（例如，使用未量化的开销），这会降低在现实世界中应用时的性能。该项目利用人工智能的最新进展，旨在开发频谱制图理论和在复杂，沉重的阴影和动态环境下使用有限的信息交换（即几个位）的方法，这些信息交换在很大程度上是未知的研究水。特别是，该项目旨在设计一类潜在神经因素分析（LANFAC）模型，以简短的方式代表RF环境。使用LANFAC模型，该项目将提供频谱制图方法，以通过有限和量化的测量来重建现实的RF环境。该项目中开发的理论和方法可能会在地球科学，食品科学，视频处理和医学成像等学科中找到广泛的应用。这项研究将加强本科教育，并为来自代表性不足和服务不足的小组的优化，深度学习，张量分析以及感知的培训机会，旨在增强其在信号和机器智能方面的职业前景。该项目将开发出可在可靠，强大的，有效的图像范围的工具的分析和计算工具的工具上，以开发一系列的分析和计算工具。建模。 LNFAC模型是潜在因子分析模型（例如张量分解）和神经生成模型的明智整合。这项工作将首先在现实的RF环境中开发有限的基于反馈和LANFAC辅助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，没有培训数据），并使用未经训练的LANFAC模型从量化的信息反馈/交换中开发可证明的频谱制图。最后的研究推力将使用精心设计的模拟器和软件定义的无线电实验来验证理论并评估算法。使用未经训练的神经模型保留了强烈的表现力，而无需依靠训练数据，这将促进分布式，交换限制和自适应频谱制图。 Real-DATA的获取和发布将有助于研究社区开发有效且可重复的光谱制图方法，并最终以集体方式提高对RF意识问题的理解。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力和更广泛的影响来通过评估来支持的，并被视为值得获得的支持。