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In our ever-expanding world of advanced satellite and communications systems, there's a growing challenge for passive radiometer sensors used in the Earth observation like 5G. These passive sensors are challenged by risks from radio frequency interference (RFI) caused by anthropogenic signals. To address this, we urgently need effective methods to quantify the impacts of 5G on Earth observing radiometers. Unfortunately, the lack of substantial datasets in the radio frequency (RF) domain, especially for active/passive coexistence, hinders progress. Our study introduces a controlled testbed featuring a calibrated L-band radiometer and a 5G wireless communication system. In a controlled chamber, this unique setup allows us to observe and quantify transmission effects across different frequency bands. By creating a comprehensive dataset, we aim to standardize and benchmark both wireless communication and passive sensing. With the ability to analyze raw measurements, our testbed facilitates RFI detection and mitigation, fostering the coexistence of wireless communication and passive sensing technologies while establishing crucial standards.
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This content will become publicly available on May 12, 2026
Robust AI-Assisted Successive Interference Cancellation for Coexistence of RF Sensors and 5G Signals in Shared Spectrum
The coexistence of active 5G communication signals and passive sensors in shared spectrum environments presents significant challenges due to radio frequency interference (RFI). This paper introduces a novel two-stage successive interference cancellation (SIC) architecture leveraging artificial intelligence (AI) to mitigate interference and preserve the integrity of passive sensing. The first stage employs a deep multilayer perceptron (D-MLP) trained with the Levenberg-Marquardt (LM) algorithm to reconstruct dominant active signals. The second stage, powered by a Bayesian Regularization (BR)-trained D-MLP, addresses residual non-linear and weak interference. Together, these stages achieve superior interference cancellation, ensuring robust separation of active and passive signals. The proposed architecture is evaluated in scenarios with varying interference complexities, including single and multiple active sources. Results demonstrate that the AI-assisted SIC framework significantly outperforms conventional methods, effectively reconstructing and removing 5G signals even under challenging conditions, such as low signal-to-noise ratios. The system also showcases adaptability, maintaining high performance when trained on one gain level and tested on another. This research advances the field by providing a scalable and robust solution for enabling reliable spectrum coexistence, particularly for Earth observation and environmental monitoring.
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- Award ID(s):
- 2332661
- PAR ID:
- 10639037
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE International Symposium on Dynamic Spectrum Access Networks
- ISSN:
- 2473-070X
- ISBN:
- 979-8-3315-3362-5
- Page Range / eLocation ID:
- 1 to 8
- Subject(s) / Keyword(s):
- Radio frequency Earth Interference cancellation 5G mobile communication Scalability Bayes methods Reliability Environmental monitoring Artificial intelligence Signal to noise ratio
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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