Radio frequency interference (RFI) have been an enduring concern in radio
astronomy, particularly for the observations of pulsars which require high
timing precision and data sensitivity. In most works of the literature, RFI
mitigation has been formulated as a detection task that consists of localizing
possible RFI in dynamic spectra. This strategy inevitably leads to a potential
loss of information since parts of the signal identified as possibly
RFI-corrupted are generally not considered in the subsequent data processing
pipeline. Conversely, this work proposes to tackle RFI mitigation as a joint
detection and restoration that allows parts of the dynamic spectrum affected by
RFI to be not only identified but also recovered. The proposed supervised
method relies on a deep convolutional network whose architecture inherits the
performance reached by a recent yet popular image-denoising network. To train
this network, a whole simulation framework is built to generate large data sets
according to physics-inspired and statistical models of the pulsar signals and
of the RFI. The relevance of the proposed approach is quantitatively assessed
by conducting extensive experiments. In particular, the results show that the
restored dynamic spectra are sufficiently reliable to estimate pulsar
times-of-arrivals with an accuracy close to the one that would be obtained from
RFI-free signals.

射电频率干扰（RFI）对射电天文学观测，尤其是对需要高时序精度和数据敏感性的脉冲星观测而言，一直是一个持久的关切。大部分文献中，RFI 的缓解被构建为一种检测任务，该任务包括在动态频谱中定位可能的 RFI。与之相反，本论文提出将 RFI 缓解作为一种联合检测与恢复的方法，这样可以不仅仅标识 RFI 影响的动态频谱的部分，还能恢复这些部分。所提出的监督方法依赖于一个深度卷积网络，其架构继承了最近流行的图像去噪网络取得的性能。为了训练该网络，建立了一个完整的模拟框架，根据脉冲星信号和 RFI 的物理启发和统计模型生成大数据集。通过进行广泛的实验来定量评估所提出方法的相关性。结果表明，恢复后的动态频谱足够可靠，可以以接近无 RFI 信号获得的精度估计脉冲星到达时间。

RFI-DRUnet：修复由射频干扰损坏的动态光谱 —— 应用于脉冲星观测

RFI-DRUnet: Restoring dynamic spectra corrupted by radio frequency  interference -- Application to pulsar observations

Semantic communication (SemCom) has emerged as a 6G enabler while promising
to minimize power usage, bandwidth consumption, and transmission delay by
minimizing irrelevant information transmission. However, the benefits of such a
semantic-centric design can be limited by radio frequency interference (RFI)
that causes substantial semantic noise. The impact of semantic noise due to
interference can be alleviated using an interference-resistant and robust
(IR$^2$) SemCom design. Nevertheless, no such design exists yet. To shed light
on this knowledge gap and stimulate fundamental research on IR$^2$ SemCom, the
performance limits of a text SemCom system named DeepSC is studied in the
presence of single- and multi-interferer RFI. By introducing a principled
probabilistic framework for SemCom, we show that DeepSC produces semantically
irrelevant sentences as the power of single- and multi-interferer RFI gets very
large. Corroborated by Monte Carlo simulations, these performance limits offer
design insights -- regarding IR$^2$ SemCom -- contrary to the theoretically
unsubstantiated sentiment that SemCom techniques (such as DeepSC) work well in
very low signal-to-noise ratio regimes. The performance limits also reveal the
vulnerability of DeepSC and SemCom to a wireless attack using RFI. Furthermore,
our introduced probabilistic framework inspires the performance analysis of
many text SemCom techniques, as they are chiefly inspired by DeepSC.

在单个和多个干扰者的无线电频率干扰的情况下，研究了名为 DeepSC 的文本 SemCom 系统的性能极限，并通过引入一个明确的概率框架来展示，当单个和多个干扰者的 RFI 功率变得非常大时，DeepSC 会产生语义上不相关的句子。这些性能极限提供了有关 IR2 SemCom 设计的设计思路，并揭示了 DeepSC 和 SemCom 对 RFI 的漏洞，同时还启发了其他文本 SemCom 技术的性能分析。

深度学习文本语义通信在干扰下的性能极限

Performance Limits of a Deep Learning-Enabled Text Semantic Communication under Interference

We propose a novel approach for mitigating radio frequency interference (RFI)
signals in radio data using the latest advances in deep learning. We employ a
special type of Convolutional Neural Network, the U-Net, that enables the
classification of clean signal and RFI signatures in 2D time-ordered data
acquired from a radio telescope. We train and assess the performance of this
network using the HIDE & SEEK radio data simulation and processing packages, as
well as early Science Verification data acquired with the 7m single-dish
telescope at the Bleien Observatory. We find that our U-Net implementation is
showing competitive accuracy to classical RFI mitigation algorithms such as
SEEK's SumThreshold implementation. We publish our U-Net software package on
GitHub under GPLv3 license.

使用深度学习技术，提出了一种用于射电数据中减轻射电频率干扰信号（RFI）的新方法，通过一种特殊的卷积神经网络，该网络可以对来自射电望远镜获得的 2D 有序时间数据中的清洁信号和 RFI 信号进行分类。作者实现的 U-Net 得到了与已有的 RFM 算法相媲美的结果。