Collaborative Research: SWIFT: SHIELD: A Software-Hardware Approach for Spectrum Coexistence with Rapid Interferer Learning, Detection, and Mitigation

在过去的二十年里，无线数据传输的增长速度惊人，影响着我们生活和国家经济的方方面面。这导致了低于6 GHz频段的频谱紧缩，而这些频段对中远程无线通信最有用。即使新的频谱被分配给不同的无线网络和系统，满足高数据速率的日益增长的需求也将导致主动用户（生成信号）和被动用户（感测用于天气感测、天文学和其他应用的信号）的共存。当这些用户共享相同的频带或当主动用户在接近被动用户所使用的频带的频带中操作时，不可避免地发生干扰。因此，迫切需要一种有效地管理和减少这种干扰的方法，这将允许进一步改善频谱使用。该项目将解决这一挑战，并专注于跨层的软件-硬件方法，用于频谱共存，具有快速的干扰学习，检测和缓解（SHIELD）。在社会范围内，拟议的研究可以提高频谱利用率，增加对按需无线数据的访问，而不会对现有用户产生不利影响，这将产生直接的经济影响。更广泛的影响还包括涉及高中生的主要外展活动，旨在扩大妇女和代表性不足的少数民族的参与，以及将新的硬件，软件和网络架构纳入本科和研究生课程。具体来说，这个跨学科项目将弥合集成电路，通信，网络和机器学习等广泛领域之间的差距，并将专注于基于新型RF硬件和网络控制架构的协同设计，实现快速的频谱学习、检测和频谱共存缓解。主要活动包括：（i）广泛的频谱测量和数据收集，用于表征潜在干扰器的频谱使用和特性，（ii）开发新型可重新配置的0.4- 4.0GHz MIMO接收机架构，该架构利用并行辅助接收机进行快速干扰检测和N路序列混合，用于对特定干扰器进行置零，以及（iii）设计智能控制平面，其集成了软件定义的网络和机器学习技术，用于跨空间分布的接收器的有效频谱监视、管理和资源分配。开发的硬件和软件将在实验室环境中进行评估，并通过在城市规模的无线测试平台中进行集成，在现实环境中进行评估。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The last two decades have witnessed enormous increase in wireless data transfer, impacting every aspect our lives and our nation's economy. This has led to a spectrum crunch in frequency bands below 6 GHz that are the most useful for intermediate and long-range wireless communications. Even as new spectrum is allocated for different wireless networks and systems, satisfying the increasing demand of high data rates will lead to coexistence of active users (that generate signals) and passive users (that sense signals for weather sensing, astronomy, and other applications). Inevitably, interference occurs when these users share the same frequency band or when active users operate in bands close to that used by passive users. Therefore, there is a critical need for an approach to efficiently manage and reduce such interference, which will allow for further improved spectrum usage. This project will address this challenge and focus on a cross-layer software-hardware approach for spectrum coexistence with rapid interferer learning, detection, and mitigation (SHIELD). On a societal scale, the proposed research can improve spectrum utilization and increase access to in-demand wireless data without adversely impacting existing users, which will have direct economic impact. The broader impacts also include major outreach activities involving high school students and aiming at broadening the participation of women and underrepresented minorities, as well as incorporation of new hardware, software, and network architecture into undergraduate and graduate classes.Specifically, this interdisciplinary project will bridge the gap between the broad areas of integrated circuits, communications, networking, and machine learning, and will focuses on enabling rapid interferer learning, detection, and mitigation for spectrum coexistence based on the co-design of novel RF hardware and network control architecture. The main activities include: (i) extensive spectrum measurements and data collection for characterizing the spectrum usage and properties of potential interferers, (ii) development of a novel reconfigurable 0.4-4.0 GHz MIMO receiver architecture leveraging a concurrent auxiliary receiver for rapid interference detection and N-path sequence-mixing for nulling specific interferers, and (iii) design of an intelligent control plane, which integrates software-defined networking and machine learning techniques, for efficient spectrum monitoring, management, and resource allocation across spatially distributed receivers. The developed hardware and software will be evaluated in the lab setting and in real-world environments through their integration in a city-scale wireless testbed.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.