LOCI: Learning Low Overhead Collaborative Interference Cancellation for Radio Astronomy
LOCI: Learning Low Overhead Collaborative Interference Cancellation for Radio Astronomy
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DOI：
10.1109/icc45041.2023.10279797
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发表时间：
2023-05
期刊：
影响因子：
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通讯作者：
Shuvam Chakraborty;D. Saha;Aveek Dutta;Gregory Hellbourg
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作者：
Shuvam Chakraborty;D. Saha;Aveek Dutta;Gregory Hellbourg
来自蜂窝和其他通信网络的射频干扰（RFI）通常在射电望远镜上减轻，而无需与干扰源进行任何主动协作。宇宙的膨胀以及地球和近地轨道通信基础设施的同步扩散正在导致前所未有的RFI，这需要协作策略来维持每个人的科学和社会目标。在这项工作中，我们开发了基于深度学习的模型，以最小的开销实现协作，同时还提供准确的RFI表征和简化的取消策略。这种多级系统设计可适应蜂窝网络产生的RFI信号的统计变化，并允许在望远镜上通过信号处理链建模（例如滤波和数字化损失）单步取消RFI。通过我们使用真实天文信号的分析和模拟，我们能够以与最先进的水平相当的精度去除蜂窝网络产生的RFI，而通信开销仅为25%，总体上将计算复杂度从O（n3）降低到O（n2）。
Radio Frequency Interference (RFI) from cellular and other communication networks is commonly mitigated at the radio telescope without any active collaboration with the interfering sources. The expanding Universe and simultaneous proliferation of Earth-based and LEO communication infrastructure is causing unprecedented RFI that require collaborative strategies to maintain the scientific and societal goals of each. In this work, we develop deep learning based models that enable collaboration with minimal overhead while also providing accurate RFI characterization and simplified cancellation strategies. This multistage system design is adaptable to changing statistics of the RFI signals generated from cellular networks and allows single step RFI cancellation by signal processing chain modeling (e.g. filtering and digitization loss) at the Telescope. Through our analysis and simulation using real astronomical signals, we are able to remove RFI generated from cellular networks with comparable accuracy to the state of the art with only 25% of the communication overhead and overall reduced computation complexity from O(n3) to O(n2).