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

本地5G，也被称为专用网络，具有巨大的经济潜力，一些研究估计，2030年其估值为360亿美元，高于2022年的11亿美元。其演进，本地6G，将主要服务于高度本地化的场景，其中无线连接在医疗保健、制造或零售等不同领域实现新的垂直应用和数字化。与传统宽带不同，这些使用案例需要可靠、有弹性且安全的连接。这可以通过反射式智能表面技术来实现，该技术可以反射或散射已经在环境中传播的信号。然而，在没有关于无线环境的足够准确的信息的情况下，反射式智能表面不能有效地引导反射信号。本研究项目致力于解决这一问题，并开发一种新的基于神经网络的信道表示方法，重点是提出本地6G的概念。为此，该项目调查了所提出方法的理论基础、实验验证及其对本地6G架构、设计和安全的影响。该项目通过让学术研究和行业社区参与美国和欧洲的预标准化活动，为6G愿景之间的洲际协调做出了贡献。其他更广泛的影响包括在劳动力发展和教育活动方面的进步，增强多样性，以及向公众传播学术成果。该项目通过引入可控、弹性和安全的本地6G来推进专用网络的概念，该本地6G具有基于神经网络的新型无线通道表示、集成了反射式智能表面的网络架构，以及基于地理围栏的安全解决方案。该项目分为三个研究推力，针对本地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号开发了一种基于神经过程的神经无线信道的新方法，用于跟踪、预测和控制无线信道。该方法的实验验证需要捕获一个独特的数据集，该数据集可用于推动人工智能应用到其他通信领域的研究。推力2开发了一种新的随机框架，用于分析本地6G的可靠性和弹性，该框架包括反射面和基于神经网络的信道表示。推力3报告了与使用反射面和基于神经网络的通道模型相关的新的安全漏洞和威胁，并提出了一种基于地理围栏的机制来解决这些安全漏洞。总体而言，拟议的研究议程侧重于新的渠道表示方法，以推进反射式智能表面技术和实现本地6G连接。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。

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.