SWIFT: Opportunistic mm-Wave Receivers

传统的无线系统工作在射频（RF）频率范围内，通常低于10 GHz。在较高频率处增加的可用带宽已经激励了使用高于10 GHz的毫米波（mm波）频率范围用于无线通信（例如，5G标准、卫星通信）、雷达（例如，汽车雷达），和成像（例如，机场扫描仪）应用。这些新兴应用与包括射电天文学在内的传统应用在相同频带上共存，这在干扰管理方面带来了挑战。共存问题尤其具有挑战性，因为许多毫米波频谱用户基于缺乏适当干扰缓解和滤波的传统技术。另一方面，为每个应用和频段开发、测试和部署专用毫米波解决方案的成本很高。该提案的重点是开发毫米波系统，该系统可以在18 - 54 GHz左右的重要频谱范围内的任何地方机会性地工作。其中关键的第一步是开发能够适应操作的收发器硬件。此外，这些接收器是基于阵列的，这允许它们也在最佳空间维度上识别和操作。与此紧密相关的是传感和自适应算法的发展，这些算法可以确定可用的频带和传输可能发生的方向。这些算法是基于新的机器学习方法，并考虑特殊的传播条件的毫米波channels.This多学科的建议提供了一个整体的研究毫米波系统，强调共存，元素，包括系统架构，信道传播，算法和集成电路设计。创新的集成电路可以在宽频谱上工作，同时提供目前仅在较低射频下实现的选择性（干扰缓解）。此外，所提出的接收器架构允许并发的多频带频率选择性以实现带内和带间载波聚合。基于机器学习的算法从一个公式开始，该公式考虑了现实世界的约束，例如传播信道的方向色散和部分稀疏性，以及不完美的硬件，并开发了新的机器学习方法来解决这些问题。定向干扰特性的大量数据将提供给所有美国研究人员。拟议研究的结果将有利于当前和未来的毫米波系统和应用，包括许可和未许可的商业无线通信、卫星通信、射电天文学和无线电力输送。研究结果将影响本科和研究生课程。PI将建立在他们广泛的记录，让本科生参与拟议的研究，并通过积极招募女性和代表性不足的少数民族参与与本项目相关的研究职位来扩大参与。研究成果通过USC Steven's Center for Innovation和衍生创业公司转化为产业。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。

Traditional wireless systems operate in the radiofrequency (RF) frequency range, which are typically below 10 GHz. The increased available bandwidth at higher frequencies has incentivized the use of millimeter-wave (mm-wave) frequency range that are above 10 GHz for wireless communications (e.g., 5G standard, satellite communications), radar (e.g., automotive radar), and imaging (e.g., airport scanners) applications. The coexistence of these emerging applications with traditional applications including radio astronomy at the same frequency bands creates challenges in terms of interference management. The coexistence issue is particularly challenging as many of the mm-wave spectrum users are based on legacy technologies that lack proper interference mitigation and filtering. On the other hand, the cost of developing, testing, and deploying dedicated mm-wave solutions for each application and frequency band is high. This proposal focuses on development of mm-wave systems that can opportunistically operate any-where within the important frequency spectrum of around 18 - 54 GHz. The first key step in it is the development of transceiver hardware that can adapt operation. Furthermore, these receivers are array based, which allows them to identify and operate at the optimum spatial dimension as well. Tightly related to this is the development of sensing and adaptation algorithms that can determine available frequency bands and directions in which transmission may occur. These algorithms are based on novel machine learning approaches and consider the special propagation conditions of millimeter-wave channels.This multidisciplinary proposal offers a holistic study of millimeter-wave systems, emphasizing coexistence, with elements that include system architecture, channel propagation, algorithms, and integrated circuit design. The innovative integrated circuits can operate across a broad frequency spectrum while providing selectivity (interference mitigation) that is currently only achieved at lower radio frequencies. Furthermore, the proposed receiver architecture allows concurrent multi-band frequency selectivity to enable for intra- and inter-band carrier aggregation. The machine-learning based algorithms start from a formulation that takes real-world constraints, such as directional dispersion and partial sparsity of the propagation channels, as well as imperfect hardware into account, and develops new machine learning approaches to tackle them. Extensive data of directional interference characteristics will be made available to all US researchers. The outcomes of the proposed study benefit current and future millimeter-wave systems and applications spanning licensed and unlicensed commercial wireless communications, satellite communications, radio astronomy, and wireless power delivery. Research results will impact undergraduate and graduate curriculum. The PIs will build on their extensive track record of involving Undergraduate students in the proposed research, and of broadening participation by actively recruiting women and underrepresented minorities for research positions related to this project. Research results be transitioned to industry via the USC Steven's Center for Innovation and spin-off startup companies.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.