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).

Direction of arrival (DoA) estimation has an important role in various applications and is widely used in modern communication, where signal power is higher than noise power for it to be decoded. Existing methods do not perform as accurately in low signal to noise ratio (SNR) as they do in higher SNR. However, passive sensing like radio astronomy or remote sensing operates at extremely low SNR, where the weakest radio frequency interference (RFI) from communication system impairs the scientific observations. Hence, it is essential to estimate the DoA of RFI at low SNR so that it can be removed at the telescopes or radiometers. In this paper, we propose a three stage algorithm that methodically exploits digital beamforming, creates virtual subarrays, inspects multiple options and introduces clustering to estimate the DoA in low SNRs. The proposed algorithm is simulated with sinusoidal as well as Automatic Dependent Surveillance-Broadcast (ADS-B) signals in Additive White Gaussian Noise (AWGN) and multipath fading channels. Experimental results show the proposed method outperforms well-known MUSIC (MUltiple SIgnal Classification) algorithm in low SNR.

Recently, there has been great interest in facilitating coexistence of active and passive users of the electromagnetic (EM) spectrum, with the primary objective of higher spectral utilization. The major challenge for passive users, such as Radio Astronomy Services (RAS), is the need for extremely quiet skies to make astronomical observations with maximum sensitivity of the radio telescope. This is increasingly difficult to guarantee because of densification of allocated spectrum, exponential growth of ubiquitous wireless communication and out-of-band astronomical observations required to observe fast radio bursts. This requires either bidirectional collaboration between active and passive users or innovative signal processing at the telescope site to cancel any incident Radio Frequency Interference (RFI). In this work, we show the feasibility of such a paradigm, where RFI from airborne sources, e.g., aircraft, LEO satellites, etc., is cancelled at the receiver of a Radio Telescope, by shaping the EM wavefront by an array of Reconfigurable Intelligent Surfaces (RIS). In contrast to conventional beam-nulling applications for RIS, this method requires precise calculation of the phase and the amplitude of the reflected signal by the RIS in order to guarantee complete cancellation of the incident RFI. We simulate this approach in a practical setting to study its error performance and boundary conditions of the system parameters, that will lead to a demonstrable prototype in near future. Our results indicate that an RIS array with 364 elements can fully cancel RFI for ADS-B systems at an elevation of$60^{\circ}$and an altitude of 10000 m.

The increasing spectrum occupancy is a serious threat for highly sensitive astronomical measurement at radio frequencies. The deployment of array radio telescopes allows the exploitation of spatial information regarding sources of radio frequency interference (RFI) in order to filter them out of astronomical data and theoretical recover uncorrupted time and frequency data. This paper introduces the corrupted array radio telescope data model, and gives an overview of array signal processing techniques considered for performing spatial RFI detection and mitigation.

Because of the denser active use of the spectrum, and because of higher radio telescope sensitivities, radio frequency interference (RFI) mitigation has become a sensitive topic for current and future radio telescope designs. In this paper, we consider different interference mitigation options which take advantage of both time-frequency and spatial RFI signatures. After specific subspace decompositions, these RFI spatial signatures are estimated and applied to pre- or post-correlation data by means of spatial filtering techniques based on projectors. We provide some performance analysis through simulations and Cramer-Rao Lower Bound derivations. In addition, recent results on real data from the LOFAR and EMBRACE radio telescopes are presented.