SWIFT: SMALL: Interference Canceling for Improved Coexistence Between Passive and Active Radio Systems

有限无线电频谱管理中的一个难题是无线电频谱的主动（发射）用户必须与被动（仅接收）用户共存；特别是射电天文学和地球物理遥感。本项目探讨了利用“取消”改善共存的前景。取消包括识别和减去不需要的信号，以便接收器能够访问感兴趣的信号，否则会被遮挡。原则上，这可以增强频谱的共享。然而，现有的消除技术一般来说不够可靠，不能广泛使用。该项目旨在使用包括机器学习在内的增强信号处理技术来提高消光器的性能。这项工作的成功将(1)提高有源和无源用户共享频谱和在相邻频带中工作的能力，同时(2)提高必须在分配给有源业务的频带内或附近工作的无源应用收集的信号质量。该项目将通过两个学期的高年级设计项目，吸引工科本科学生作为研究人员。公众宣传将包括一个面向公众的网站和一个YouTube频道，记录该项目的各种活动。本项目采用的研究策略是解决过去已经发现限制被动用户取消性能的两个主要困难，即：(1)干扰可能同时具有破坏性，但太弱而无法准确估计，以及(2)干扰信号在必须取消的时间尺度上变化很大。“太弱而无法估计”的问题将通过使用信号的部分跨层知识、机器学习和地面源站点接收器来解决。“动态通道”问题将通过增强物理模型和机器学习来改善参数跟踪来解决。增强的消光器有望更好地减轻主要发射相对于观测频率位于带外的干扰的频谱副瓣。此外，机器学习将用于动态预测性能，并根据用户和应用程序特定的目标推荐取消策略。性能将通过使用模拟、从操作仪器收集的真实数据以及使用位于弗吉尼亚理工大学的专用小口径望远镜试验台进行演示来进行评估。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智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

A difficult problem in the management of the limited radio spectrum is that active (transmitting) users of the radio spectrum must coexist with passive (receive-only) users of the radio spectrum; in particular, with radio astronomy and geophysical remote sensing. This project investigates the prospects for improving coexistence using "canceling". Canceling consists of identifying and subtracting an undesired signal such that the receiver is able to access signals of interest that would otherwise be obscured. In principle, this allows enhanced sharing of spectrum. However, existing canceler technology is generally not sufficiently reliable for general use. This project aims to improve the performance of cancelers using enhanced signal processing techniques including machine learning. Success in this effort will (1) improve the ability of active and passive users to share spectrum and to operate in adjacent frequency bands while (2) improving the quality of signals collected in passive applications who must operate in or near bands allocated to active services. The project will engage undergraduate engineering students as researchers through two-semester senior design projects. Public outreach will include a public-facing website and a YouTube channel documenting the various activities of the project.The research strategy employed in this project is to address the two principal difficulties that have been found to limit the performance of canceling for passive users in the past, namely (1) that interference can be simultaneously damaging yet too weak to accurately estimate, and (2) interfering signals vary significantly over the timescales over which they must be canceled. The "too weak to estimate" problem will be addressed using concurrent partial cross-layer knowledge of the signal, machine learning, and terrestrial source-sited receivers. The "dynamic channel" problem will be addressed using augmented physical models and machine learning to improve tracking of parameters. Enhanced cancelers are expected to be able to better mitigate the spectral sidelobes of interferers whose primary emission is located out-of-band relative to the frequency of observation. Further, machine learning will be used to dynamically predict performance and recommend canceling strategies tailored to user- and application-specific objectives. Performance will be evaluated through the use of simulations, bona fide data collected from operational instruments, and demonstrations using a purpose-built small-aperture telescope testbed located at Virginia Tech.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.