SWIFT: Opportunistic mm-Wave Receivers

传统的无线系统在射频（RF）频率范围内工作，通常低于10ghz。更高频率下可用带宽的增加刺激了无线通信（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）应用中使用10 GHz以上的毫米波频率范围。这些新兴应用与传统应用（包括射电天文学）在同一频段共存，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的重要频谱范围内的任何地方运行。其中关键的第一步是开发适应操作的收发器硬件。此外，这些接收器是基于阵列的，这使得它们能够在最佳空间维度上识别和运行。与此密切相关的是传感和自适应算法的发展，这些算法可以确定可能发生传输的可用频段和方向。这些算法基于新颖的机器学习方法，并考虑了毫米波信道的特殊传播条件。这个多学科的提案提供了毫米波系统的整体研究，强调共存，包括系统架构、信道传播、算法和集成电路设计等要素。创新的集成电路可以在广泛的频谱范围内工作，同时提供选择性（干扰缓解），目前只能在较低的无线电频率上实现。此外，所提出的接收器架构允许并发多频带频率选择性，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，例如传播通道的定向分散和部分稀疏性，以及不完善的硬件，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。拟议研究的结果有利于当前和未来的毫米波系统和应用，涵盖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传输。研究结果将影响本科和研究生课程。pi将以其广泛的记录为基础，让本科生参与拟议的研究，并通过积极招募妇女和代表性不足的少数民族担任与本项目相关的研究职位来扩大参与。研究成果将通过南加州大学史蒂文创新中心和衍生创业公司转化为工业。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。

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.