LOCI: Learning Low Overhead Collaborative Interference Cancellation for Radio Astronomy

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(n(3)) to O(n(2)).