CNS Core: Small: Secured Spectrum Allocation and Patrolling in Shared Spectrum Systems

射频频谱是一种需求量很大的自然资源，也是巨大的经济驱动力。共享频谱系统在优化频谱利用率方面提供了巨大的前景，它可以在不损害许可用户的情况下为非许可用户分配频谱。然而，由于缺乏对地理区域内信号传播特性的了解，这类系统中的频谱管理是具有挑战性的。因此，这种系统中的频谱分配要么非常保守，要么基于不完美的模型。有效的频谱利用还取决于防范未经授权的使用攻击的能力；然而，当前的技术是有限的。该项目的目标是通过在众感式体系结构中利用大量频谱传感器来开发有效的频谱管理技术。总体而言，频谱效率的提高可能会影响许多不同的领域，如物联网、教育、网络物理系统安全、医疗保健、媒体和娱乐，这些领域未来有巨大的频谱需求，创新可能会因为缺乏足够的带宽而停止。因此，通过有效的分配和巡逻来提高频谱利用率必然会产生显著的经济效益。该项目预计将为行业提供技术投入，并向监管机构通报最佳实践和权衡。该项目将通过利用大量众包频谱传感器，为共享频谱系统开发高效和安全的频谱分配和巡逻技术。该项目的重点将是使用深度学习技术来消除假设传播模型的需要。本课题的研究主题如下：1.频谱分配。该项目将开发高效的监督学习技术，以在一般情况下学习频谱分配功能。2.隐私保护频谱管理协议。为了最大限度地参与众包感知模型，必须保护参与频谱管理的实体的隐私。因此，该项目将开发向所有系统实体提供隐私的高效加密协议。3.频谱巡逻。防止未经授权访问共享频谱是提高频谱利用率的关键。因此，该项目将通过开发有效的深度学习方法来定位入侵者，从而开发有效的技术来保护频谱免受未经授权的访问。4.评价。该项目将通过三个模拟平台对整个系统进行评估，包括：(I)小型室内和室外试验台，以及(Ii)将使用无人机收集的信道状态数据的模拟平台。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。

The RF spectrum is a natural resource in great demand and a tremendous economic driver. Shared spectrum systems, which facilitate spectrum allocation to unlicensed users without harming the licensed users, offer great promise in optimizing spectrum utility. However, spectrum management in such systems is challenging mainly due to the lack of knowledge of signal propagation characteristics in the geographic areas. Thus, spectrum allocation in such systems is either done very conservatively or is based on imperfect models. Efficient spectrum utilization also depends on the ability to guard against unauthorized usage attacks; however, the current techniques are limited. The goal of this project is to develop effective spectrum management techniques by leveraging a large number of spectrum sensors in a crowdsensed architecture. In general, improved spectrum efficiency could impact many diverse fields such as IoT, education, cyber-physical systems security, healthcare, media and entertainment, which have tremendous future spectrum needs and innovation is perhaps halted due to lack of enough bandwidth. Thus, improved spectrum utilization via efficient allocation and patrolling is bound to yield significant economic benefits. This project is expected to provide technology inputs for industry and inform regulators about best practices and tradeoffs.This project will develop efficient and secured techniques for spectrum allocation and patrolling, for a shared spectrum system, by leveraging a large number of crowdsourced spectrum sensors. The project’s focus will be on using deep-learning techniques to obviate the need to assume a propagation model. The project has the following research themes: 1. Spectrum Allocation. The project will develop efficient supervised learning techniques to learn the spectrum allocation function, in general settings. 2. Privacy Preserving Spectrum Management Protocols. To maximize participation in a crowdsourced sensing model, it is imperative to preserve privacy of the entities involved in the spectrum management. Thus, the project will develop efficient cryptographic protocols that provide privacy to all system entities. 3. Spectrum Patrolling. Preventing unauthorized access of the shared spectrum is key to improved spectrum utilization. Thus, the project will develop efficient techniques to protect the spectrum from unauthorized access, by developing effective deep learning approaches to localize intruders. 4. Evaluation. The project will evaluate the overall system, over three simulation platforms including: (i) small indoor and outdoor testbeds, and (ii) a simulation platform that will use channel-state data gathered by drones.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.