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

该项目正在帮助为更高效、更安全地访问无线频谱奠定基础，同时保护该频谱的基本现有用途。这种接入对于满足传统和新兴无线应用的需求至关重要，这些应用在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(DASAS)开发新的传感器、算法解决方案、模型和架构，这将扩展当前最先进的技术，使数据驱动的频谱接入解决方案成为可能。这个项目的其他影响包括扩展到K-12教育，培训和指导学生，包括本科生和未被充分代表的少数族裔，以及开发与拟议研究相关的新课程。设想的系统将利用广泛的低成本传感来支持在不同时间尺度的不同频段之间的频谱共享，用于丰富的不同用例，包括静态和移动用户、主动和被动频谱利用率，以及计划和随机使用。我们正在采取多学科的方法来设计这个系统，包括：开发使用新的低功率分布式算法的低成本频谱传感器；了解如何有效地将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)进行集成和查询，并使用它来预测性地触发频谱分配决策的算法；开发新的可扩展的频谱分配算法，使用获得的数据管理广泛的频谱用户，同时考虑到市场竞争；并确保所产生的系统可以利用可用的数据来保护其免受恶意攻击者和无意用户行为的攻击。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。

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.