SpecEES: Cooperative Green RF Sensing over Shared Spectrum

雷达占据了6千兆赫以下频谱的很大一部分。这是一个非常拥挤的频段，也被无线通信大量使用。近年来，无线部门越来越多地要求释放更多政府持有的频谱，包括分配给雷达的频谱，以扩大移动宽带能力，促进无线创新，并提振经济。尽管如此，雷达利益攸关方往往不愿分享他们的频谱，因为缺乏激励措施，以及担心来自无线通信设备的干扰可能扰乱雷达运行。该项目考虑了雷达和无线通信之间的这种频谱共享方案，并提出了一系列旨在激励雷达利益攸关方开放频谱共享的研究工作。具体地说，它将开发新的频谱共享协议和合作射频(RF)传感技术，以通过共享所赋予的能效和增强的传感能力来保护、保护和激励雷达用户。预计拟议的研究将提供双赢的解决方案，不仅使雷达能够以更高的能效更好地检测、成像、识别和跟踪目标，还将使无线设备享受显著改善的频谱接入机会。在教育方面，该项目将提供课程开发、本科生和研究生培训、针对当地高中生的暑期研究项目以及从代表不足的群体中招收学生的机会。该项目针对的是频谱共享场景，即在不影响雷达运行的情况下，雷达拥有并愿意与无线通信用户共享其频谱。拟议的努力通过以下四项研究工作整合在一起：(1)以雷达为中心的频谱共享协同设计框架，它在保护雷达的同时改进无线通信；(2)合作照明方法，允许雷达利用无线通信源作为被动照明器；(3)合作射频感测范例，其中雷达和多个频谱共享通信设备协同执行分布式射频感测任务；以及(4)与雷达和通信交叉设计的安全和隐私，该交叉设计利用集成的加密凭据和非加密设备指纹的新的混合认证方案。建议的射频传感框架允许雷达建设性地使用同频道通信源作为被动照明器，提供免费的照明功率，更重要的是，额外的多静态传感几何结构，使雷达能够实现增强的传感能力和能量效率，即绿色射频传感。为了解决拟议框架中固有的安全和隐私问题，该项目将开发一种新的混合身份验证方案，该方案利用独特的设备指纹和数据分析工具(包括深度学习)来确保拟议的频谱共享和合作传感解决方案的安全。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。

Radar occupies a significant fraction of the spectrum below 6 GHz. This is a highly congested frequency band also heavily used by wireless communications. In recent years, there has been a growing demand from the wireless sector to free up more of the government-held spectrum, including those allocated to radar, in order to expand mobile broadband capabilities, promote wireless innovation, and boost the economy. Nonetheless, radar stakeholders are often unwilling to share their spectrum due to lack of incentives, as well as concerns over interference from the wireless communication devices that can disrupt the radar operation. This project considers such a spectrum-sharing scenario between radar and wireless communications and proposes a set of research efforts that are aimed to motivate radar stakeholders to open their spectrum for sharing. Specifically, it will develop new spectrum sharing protocols and cooperative radio frequency (RF) sensing techniques that protect, secure, and incentivize the radar users with reward of energy efficiency and enhanced sensing capabilities bestowed by sharing. The proposed research is expected to provide win-win solutions that will not only enable the radar to better detect, image, identify, and track targets with higher energy efficiency, but also allow wireless devices to enjoy significantly improved spectrum access opportunities. On the educational front, this project will offer opportunities for curricula development, training of undergraduate and graduate students, summer research programs targeting local high school students, and recruitment of students from underrepresented groups to this project.This project targets a spectrum sharing scenario, where radar owns and is willing to share its spectrum with wireless communication users under the condition that the radar operation is not to be compromised. The proposed efforts are integrated through the following four research thrusts: (1) a radar-centric cooperative design framework for spectrum sharing that protects the radar while improving the wireless communications; (2) a cooperative illumination approach that allows the radar to exploit wireless communication sources as passive illuminators; (3) a cooperative RF sensing paradigm whereby the radar and multiple spectrum-sharing communication devices cooperatively perform a distributed RF sensing task; and (4) security and privacy with radar and communication cross design that capitalizes on a new hybrid authentication scheme using integrated cryptographic credentials and non-cryptographic device fingerprints. The proposed RF sensing framework allows the radar to constructively employ co-channel communication sources as passive illuminators, which provide free illumination power and, more importantly, additional multi-static sensing geometries that enable the radar to achieve enhanced sensing capability and energy efficiency, i.e., the green RF sensing. To address the security and privacy issues inherent in the proposed framework, this project will develop a new hybrid authentication scheme that leverages unique device fingerprints and data analytics tools including deep learning to secure the proposed spectrum sharing and cooperative sensing solutions.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.