Collaboration With Cellular Networks for RFI Cancellation at Radio Telescope

Collaboration With Cellular Networks for RFI Cancellation at Radio Telescope
复制标题

DOI:
10.1109/tccn.2023.3242360
复制
发表时间:
2022-10
影响因子:
8.6
通讯作者:
Shuvam Chakraborty;G. Hellbourg;M. Careem;D. Saha;Aveek Dutta
Shuvam Chakraborty;G. Hellbourg;M. Careem;D. Saha;Aveek Dutta
中科院分区:
计算机科学2区
文献类型:
--
作者:
Shuvam Chakraborty;G. Hellbourg;M. Careem;D. Saha;Aveek Dutta

文献摘要

相似文献

随着对电磁频谱支持下一代(xG)通信网络的需求不断增长,在射电天文学的受保护频段中产生了越来越多的不必要的射频干扰(RFI)。射频干扰通常在射电望远镜上得到缓解,而不需要与干扰源进行任何积极的合作。在这项工作中,我们提供了一种方法的信号表征及其使用在随后的消除,使用来自望远镜和发射机信号的特征空间。这与传统的时频域分析不同,传统的时频域分析限于固定的表征(例如,傅立叶方法中的复指数)不能适应变化的统计(例如,自相关),通常在通信系统中观察到。我们已经提出了这种协作方法的有效性,使用真实世界的天文信号和实际模拟的LTE信号(下行链路和上行链路)作为RFI源沿着基于预设基准和标准的传播条件。通过我们使用这些信号的分析和模拟,我们能够从蜂窝网络中去除89.04%的RFI,这减少了望远镜的切除,并且能够显着提高吞吐量,因为损坏的时间频率箱数据变得可用。
The growing need for electromagnetic spectrum to support the next generation (xG) communication networks increasingly generate unwanted radio frequency interference (RFI) in protected bands for radio astronomy. RFI is commonly mitigated at the Radio Telescope without any active collaboration with the interfering sources. In this work, we provide a method of signal characterization and its use in subsequent cancellation, that uses Eigenspaces derived from the telescope and the transmitter signals. This is different from conventional time-frequency domain analysis, which is limited to fixed characterizations (e.g., complex exponential in Fourier methods) that cannot adapt to the changing statistics (e.g., autocorrelation) of the RFI, typically observed in communication systems. We have presented effectiveness of this collaborative method using real-world astronomical signals and practical simulated LTE signals (downlink and uplink) as source of RFI along with propagation conditions based on preset benchmarks and standards. Through our analysis and simulation using these signals, we are able to remove 89.04% of the RFI from cellular networks, which reduces excision at the Telescope and is capable of significantly improving throughput as corrupted time-frequency bins of data become usable.