Collaborative Research: SWIFT: Collaborative Interference Cancellation for Radio Astronomy

协作研究：SWIFT：射电天文学协作干扰消除

• 批准号: 2128497
• 负责人: Gregory Hellbourg
• 金额: $ 12.45万
• 依托单位: California Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2128497&HistoricalAwards=false
• 关键词: Collaborative; Research; SWIFT; Interference; Cancellation

This project seeks to enhance coexistence in shared frequency bands between wireless cellular networks and passive radio telescopes. This project helps address challenges created by growing congestion of the radiofrequency spectrum. If successful, the project will improve radio telescope capability through reducing interference problems. It will reduce barriers to further deployment of wireless networks, thus facilitating the high bandwidth reliable and ubiquitous connectivity needed for continued growth in the benefits of telecommunications. Interference problems between communications and radio telescopes have existed for a long time. One approach studied to overcome them has been active interference cancellation, in which the telescope receiver estimates the interfering communications signal then subtracts it from the astronomical measurement in order to expose the desired information. Most prior work on active cancellation assumes no cooperation from the cellular networks; this work studies how the telescope and cellular network could work together to improve cancellation performance. Most prior work measures the interfering signals at the telescope site, while this work measures the interfering signals at their transmission location; most prior work uses external spectrum monitoring devices, while this work characterizes the interference in the digital domain of the transmitter; these attributes of the approach give a clearer picture of the interfering signals at much less cost, but also create challenges that the project will seek to overcome.The approach used focuses on active interference cancellation at the telescope supported by active bidirectional collaboration between the telescope and neighboring cellular networks. Key aspects include: characterize cellular system signals using software running inside the cellular base stations that analyzes the digital representation of the transmissions; decompose the cellular signals at the base stations into an orthogonal stochastic component representation (the Karhunen-Loeve Transformation); perform the decomposition using a neural network architecture with domain specific knowledge to combine high accuracy and computational efficiency; intelligently aggregate information about multiple emitters in the cellular network using topology aware distributed learning; perform the same decomposition on the astronomical signal at the telescope then combine it with the aggregated information about the cellular signals to cancel the interference; and use continuous quantitative feedback from the cancellation algorithms at the telescope to the cellular network to optimize the performance of the decomposition and aggregation algorithms. The research will be validated with a prototype deployment of the interference measurement and cancellation apparatus at the Deep Synoptic Array telescope (DSA-110) at the Owens Valley Radio Observatory.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.

该项目旨在加强无线蜂窝网络和无源射电望远镜之间共享频段的共存。该项目有助于解决射频频谱日益拥挤所带来的挑战。如果成功，该项目将通过减少干扰问题来提高射电望远镜的能力。它将减少进一步部署无线网络的障碍，从而促进电信效益持续增长所需的高带宽、可靠和无处不在的连接。通信和射电望远镜之间的干扰问题已经存在了很长时间。研究克服这些问题的一种方法是主动干扰消除，其中望远镜接收器估计干扰通信信号，然后从天文测量中减去它，以便暴露所需的信息。大多数关于主动抵消的先前工作假设没有来自蜂窝网络的合作;这项工作研究了望远镜和蜂窝网络如何一起工作以提高抵消性能。大多数先前的工作在望远镜站点测量干扰信号，而这项工作在其传输位置测量干扰信号;大多数先前的工作使用外部频谱监测设备，而这项工作表征发射机数字域中的干扰;该方法的这些属性以低得多的成本给出了干扰信号的更清晰的图像，但也带来了该项目将寻求克服的挑战。所使用的方法侧重于在望远镜和相邻蜂窝网络之间的主动双向协作支持下在望远镜处进行主动干扰消除。主要方面包括：使用在蜂窝基站内部运行的软件来表征蜂窝系统信号，该软件分析传输的数字表示;（Karhunen-Loeve变换）;使用具有领域特定知识的神经网络架构来执行分解，以将高精度和计算效率联合收割机结合起来;在望远镜处对天文信号执行相同的分解，然后将其与关于蜂窝信号的聚合信息联合收割机组合以消除干扰;并使用从望远镜处的抵消算法到蜂窝网络的连续定量反馈来优化分解和聚集算法的性能。该研究将通过欧文斯谷射电天文台的深层天气阵列望远镜（DSA-110）的干扰测量和消除设备的原型部署进行验证。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。