NSF-AoF: NeTS: Small: Local 6G Connectivity: Controlled, Resilient, and Secure (6G-ConCoRSe)

NSF-AoF：NetS：小型：本地 6G 连接：受控、弹性和安全 (6G-ConCoRSe)

• 批准号: 2326599
• 负责人: Jacek Kibilda
• 金额: $ 59.99万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-01 至 2026-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2326599&HistoricalAwards=false
• 关键词: NSF; AoF; NeTS; Small; Local

Local 5G, otherwise known as private networking, has a huge economic potential, with some studies estimating its valuation at $36 billion in 2030, up from $1.1 billion in 2022. Its evolution, local 6G, will predominantly serve highly localized scenarios where wireless connectivity enables new vertical applications and digitalization in fields as diverse as healthcare, manufacturing, or retail. Unlike conventional broadband, these use cases require reliable, resilient, and secure connectivity. This can be achieved with the reflective intelligent surface technology that can reflect or scatter signals already propagating in the environment. However, reflective intelligent surfaces cannot effectively direct the reflected signals without sufficiently accurate information about the wireless environment. This research project focuses on addressing this issue and developing a new neural network-based method for channel representation with a focus on advancing the concept of local 6G. To this end, the project investigates the theoretical foundations of the proposed method, experimental validation, and its implications for local 6G architecture, design, and security. The project contributes to an inter-continental alignment between visions for 6G by engaging the academic research and industry communities involved in pre-standardization activities in the U.S. and Europe. Other broader impacts include advances in workforce development and educational activities, enhancing diversity, and disseminating academic results to the public.This project advances the concept of private networks by introducing controllable, resilient, and secure local 6G that features novel neural network-based wireless channel representation, a network architecture that integrates reflective intelligent surfaces, and security solutions based on geofencing. The project is divided into three research thrusts, addressing different pillars of local 6G: controllability (Thrust 1), resiliency (Thrust 2), and security (Thrust 3). Thrust 1 develops a novel method of Neural Wireless Channel, based on neural processes, to track, predict, and control the wireless channel. The experimental validation of the method entails capturing a unique dataset that may be used to advance research in artificial intelligence applications to other areas of communications. Thrust 2 develops a novel stochastic framework for analyzing reliability and resiliency in local 6G that comprises reflective surfaces and neural network-based channel representation. Thrust 3 reports on new security vulnerabilities and threats associated with using reflective surfaces and neural network-based channel models and proposes a mechanism based on geofencing to address these security gaps. Overall, the proposed research agenda focuses on the new channel representation methods to advance reflective intelligent surface technology and enable local 6G connectivity.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.

本地5G（也称为私人网络）具有巨大的经济潜力，一些研究估计其估值在2030年的360亿美元，高于2022年的11亿美元。其进化，本地6G将主要提供高度局部的场景，无线连接性在无线连接性中可以实现新的垂直应用程序和在医疗保健等多样性的领域中，或者是零售。与常规宽带不同，这些用例需要可靠，弹性和安全的连接性。这可以通过反射性智能表面技术来实现，该技术可以反映或分散已经在环境中传播的信号。但是，如果没有足够准确的有关无线环境的信息，反射性智能表面就无法有效地指导反射信号。该研究项目着重于解决此问题并开发一种新的基于神经网络的方法，用于渠道表示，重点是推进本地6G的概念。为此，该项目研究了所提出方法的理论基础，实验验证及其对本地6G架构，设计和安全性的影响。该项目通过与参与美国和欧洲的预标准化活动的学术研究和行业社区参与，有助于6G的愿景之间的洲际一致性。其他更广泛的影响包括劳动力发展和教育活动的进步，增强多样性并将学术成果传播给公众。该项目通过引入可控，弹性和安全的本地6G来推进私人网络的概念，该项目具有新型的基于神经网络的无线渠道的新型无线渠道代表，该网络体系结构是一种基于智能表面和安全性的网络体系结构，并集成了基于地理位置和地理位置。该项目分为三个研究推力，解决了本地6G的不同支柱：可控性（推力1），弹性（推力2）和安全性（推力3）。推力1基于神经过程开发了一种新颖的神经无线通道方法，以跟踪，预测和控制无线通道。该方法的实验验证需要捕获一个独特的数据集，该数据集可用于将人工智能应用程序的研究推向其他通信领域。推力2开发了一个新型的随机框架，用于分析局部6G的可靠性和弹性，该框架包括反射性表面和基于神经网络的通道表示。推力3报告与使用反射表面和基于神经网络的渠道模型相关的新安全漏洞和威胁，并提出了一种基于地理上的机制来解决这些安全差距。总体而言，拟议的研究议程重点介绍了推进反光智能表面技术并实现本地6G连接的新渠道表示方法。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响来通过评估来支持的。