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

管理有限的无线电频谱的一个难题是，无线电频谱的主动(发射)用户必须与无线电频谱的被动(仅接收)用户共存；特别是与射电天文学和地球物理遥感共存。这个项目调查了使用“取消”来改善共存的前景。消除包括识别和减去不想要的信号，以便接收器能够访问否则将被遮挡的感兴趣的信号。原则上，这允许加强频谱共享。然而，现有的抵消器技术一般不够可靠，不能普遍使用。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抵消器的性能。这一努力的成功将(1)提高主动和被动用户共享频谱和在相邻频段操作的能力，同时(2)提高在必须在分配给主动服务的频段或附近操作的被动应用中收集的信号的质量。该项目将通过为期两个学期的高级设计项目，聘请工科本科生作为研究人员。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。该项目采用的研究战略是解决过去发现的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，无法准确估计；(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”的问题将使用信号、机器学习和地面源定位的接收器的并发部分跨层知识来解决。“动态通道”问题将使用增强的物理模型和机器学习来解决，以改进参数跟踪。增强型消除器有望更好地缓解初级辐射相对于观测频率位于带外的干扰源的光谱旁瓣。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的特定目标推荐取消策略。绩效将通过使用模拟、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门建造的小口径望远镜试验台进行演示来评估。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。

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.