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  • Evaluation of Conventional Radio Frequency Interference Detection Algorithms in the Presence of 5G Signals in a Controlled Testbed
Title: Evaluation of Conventional Radio Frequency Interference Detection Algorithms in the Presence of 5G Signals in a Controlled Testbed
Emerging communication services and satellite system deployments pose heightened interference challenges for crucial passive radiometer sensors used in environmental and atmospheric sensing. Therefore, there is an urgent necessity to develop effective approaches for detecting, mitigating, and characterizing the influence of anthropogenic sources, commonly referred to as radio frequency interference (RFI) on passive Earth-observing microwave radiometers. Experimenting the co-existence of active communication and passive sensing systems would greatly benefit from a thorough and realistic dataset covering a wide range of scenarios. The insufficient availability of extensive datasets in the radio frequency (RF) domain, particularly in the context of active/passive coexistence, poses a significant obstacle to progress. This limitation is particularly notable in the context of comprehending the effectiveness of conventional model-based RFI detection approaches when applied to advanced 5th-generation (5G) wireless communication signals. This study first shows the development of an experimental passive radiometer and 5G testbed system and aims to assess the efficacy of the widely employed spectral kurtosis RFI detection approach within controlled anechoic chamber experiments. Our experimental setup comprises a fully calibrated SDR-based L-band radiometer subjected to diverse 5 G wireless signals, varying in power levels, frequency resource block group allocation, and modulation techniques. Significantly, our testbed facilitates the concurrent recording of ground truth temperatures while subjecting the radiometer to 5 G signal transmission which helps to understand the overall effect in the radiometer. This distinctive configuration provides insights into the effectiveness of traditional RFI detection models, offering valuable perspectives on the associated challenges in RFI detection.  more » « less
Award ID(s):
2332661 2332662
PAR ID:
10541375
Author(s) / Creator(s):
; ; ; ; ;
Publisher / Repository:
IEEE
Date Published:
ISSN:
2473-070X
ISBN:
979-8-3503-1764-0
Page Range / eLocation ID:
27 to 32
Subject(s) / Keyword(s):
Microwave radiometer remote sensing deep learning RFI 5G active-passive spectrum coexistence
Format(s):
Medium: X
Location:
Washington, DC, USA
Sponsoring Org:
National Science Foundation
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