Collaborative Research: SWIFT: Collaborative Interference Cancellation for Radio Astronomy

该项目旨在加强无线蜂窝网络和无源射电望远镜在共享频段内的共存。该项目有助于应对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由来已久。一种被研究的克服它们的方法是主动干扰消除，在这种方法中，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。大多数以前的有源对消工作都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协同工作来提高对消性能。大多数以前的工作是测量望远镜现场的干扰信号，而这项工作是测量传输位置的干扰信号；大多数以前的工作使用外部频谱监测设备，而这项工作表征了发射机数字域的干扰；该方法的这些属性以更低的成本提供了更清晰的干扰信号图景，但也带来了项目将寻求克服的挑战。所使用的方法集中在望远镜和相邻蜂窝网络之间的主动双向协作支持下的有源干扰消除。主要方面包括：使用在蜂窝基站内运行的分析传输的数字表示的软件来表征蜂窝系统信号；将基站处的蜂窝信号分解成正交随机分量表示(卡尔胡宁-洛夫变换)；使用具有领域特定知识的神经网络结构来执行分解，以结合高精度和计算效率；使用拓扑感知分布式学习来智能地聚集关于蜂窝网络中的多个发射器的信息；在望远镜上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集信息组合以消除干扰；并使用从望远镜的抵消算法到蜂窝网络的连续定量反馈来优化分解和聚合算法的性能。这项研究将通过欧文斯山谷射电天文台深层天气阵列望远镜(DSA-110)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 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.