Collaborative Research: SWIFT: LARGE: Dynamics and Security Aware Predictive Spectrum Sharing with Active and Passive Users

该项目有助于为更高效、更安全的无线频谱接入奠定基础，同时保护该频谱的基本现有用途。这种访问对于满足传统和新兴无线应用的需求至关重要，这对整个社会都有潜在的好处。为此，我们正在为未来的数据感知频谱接入系统（DASASs）开发新型传感器、算法解决方案、模型和架构，这将扩展当前最先进的技术，使频谱接入的数据驱动解决方案成为可能。该项目的其他影响包括扩展到K-12教育，培训和指导学生，包括本科生和代表性不足的少数民族，以及开发与拟议研究相关的新课程。设想中的系统将利用广泛的低成本传感来支持不同时间尺度下不同频段的频谱共享，以满足一系列丰富的不同用例，包括静态和移动用户、主动和被动频谱利用以及计划使用和随机使用。我们正在采用多学科方法来设计该系统，包括：开发利用新型低功耗分布式算法的低成本频谱传感器；了解如何有效地整合和查询从这些算法获得的数据与其他来源（例如，天气，需求的空间密度），并使用它来预测触发算法频谱分配决策；开发新的可扩展的频谱分配算法，利用获取的数据管理频谱的广泛用户，同时考虑市场竞争；并确保生成的系统可以利用可用数据来确保安全，以抵御恶意攻击者和无意的用户行为。该奖项反映了美国国家科学基金会的法定使命，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。

This project is helping to lay the foundation for more efficient and secure access to wireless spectrum while protecting essential existing uses of that spectrum. This access is critical to meet the demands from both legacy and emerging wireless applications, that have potential benefits across society. To this end, we are developing novel sensors, algorithmic solutions, models and architecture for future Data-Aware Spectrum Access Systems (DASASs) that will extend the current state-of-the-art to enable date driven solutions for spectrum access. Other impacts of this project include outreach to K-12 education, training and mentoring students including undergraduates and under-represented minorities, and developing new courses related to the proposed research.The envisioned system will leverage extensive low-cost sensing to support spectrum sharing across different frequency bands at different timescales for a rich set of different use cases including static and mobile users, active and passive spectrum utilization, and planned versus random uses. We are taking a multidisciplinary approach to design this system including: developing low cost spectrum sensors that utilize novel low-power distributed algorithms; understanding how to efficiently integrate and query the data obtained from these algorithms with other sources (e.g., weather, spatial density of demands) and use it to predictively trigger algorithms for spectrum allocation decisions; developing new and scalable spectrum allocation algorithms that use the acquired data to manage a wide range of users of the spectrum while accounting for market competition; and ensuring that the resulting system can leverage the available data to be secure against malicious attackers and unintentional user behavior.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.