There is insufficient wireless frequency spectrum to support the continued growth of active wireless technologies and devices. This has provoked extensive research on spectrum coexistence. One case that has gained limited attention in this course is using currently banned frequency bands for active wireless communications. One such option is the 27 MHz-wide narrowband portion of the L-band from 1.400 to 1.427 GHz, which is exclusively devoted to space-borne passive radiometry for remote sensing and radio astronomy. Radio regulations currently prohibit active wireless communications and radars from operating in this band to avoid radio frequency interference (RFI) on highly noise-sensitive passive radiometry equipment. The National Aeronautics and Space Administration’s (NASA’s) Soil Moisture Active Passive (SMAP) satellite is one of the latest space-borne remote sensing missions that evaluates global soil moisture by passive scanning of the thermal emissions of the earth in this frequency band. In this paper, we investigate the opportunistic temporal use of this 27 MHz-wide passive radiometry band for active wireless transmissions when there is no Line of Sight (LoS) between SMAP and a terrestrial wireless network. We use MATLAB simulations to determine the fraction of time that SMAP has LoS (and non-LoS) with a terrestrial wireless cell at different Earth latitudes based on SMAP’s orbital characteristics. We also investigate the severity of RFI induced on SMAP in the presence of a terrestrial cluster of 5G cells with LoS.
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Deep Learning Based RFI Detection and Mitigation for SMAP Using Convolutional Neural Networks
Passive Remote Sensing services are indispensable in modern society because of the applications related to climate studies and earth science. Among those, NASA’s Soil Moisture Active and Passive (SMAP) mission provides an essential climate variable such as the moisture content of the soil by using microwave radiation within protected band over 1400-1427 MHz. However, because of the increasing active wireless technologies such as Internet of Things (IoT), unmanned aerial vehicles (UAV), and 5G wireless communication, the SMAP’s passive observations are expected to experience an increasing number of Radio Frequency Interference (RFI). RFI is a well-documented issue and SMAP has a ground processing unit dedicated to tackling this issue. However, advanced techniques are needed to tackle the increasing RFI problem for passive sensing systems and to jointly coexist communication and sensing systems. In this paper, we apply a deep learning approach where a novel Convolutional Neural Network (CNN) architecture for both RFI detection and mitigation is employed. SMAP Level 1A spectrogram of antenna counts and various moments data are used as the inputs to the deep learning architecture. We simulate different types of RFI sources such as pulsed, CW or wideband anthropogenic signals. We then use artificially corrupted SMAP Level 1B antenna measurements in conjunction with RFI labels to train the learning architecture. While the learned detection network classifies input spectrograms as RFI or no-RFI cases, the mitigation network reconstructs the RFI mitigated antenna temperature images. The proposed learning framework both takes advantage of the existing SMAP data and the simulated RFI scenarios. Future remote sensing systems such as radiometers will suffer an increasing RFI problem and spectrum sharing and techniques that will allow coexistance of sensing and communication systems will be utmost importance for
both parties. RFI detection and mitigation will remain a prerequisite for these radiometers
and the proposed deep learning approach has the potential to provide an additional perspective to existing solutions. We will present detailed analysis on the selected deep learning architecture, obtained RFI detection accuracy levels and RFI mitigation performance.
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- NSF-PAR ID:
- 10357949
- Date Published:
- Journal Name:
- RFI Workshop 2022
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Passive remote sensing services are indispensable in modern society as they provide crucial information for Earth science and climate studies. In parallel, modern society also depends heavily on active wireless communication technologies for daily routines, with emerging technologies such as 5G further increasing this dependence. Unfortunately, the growth of active wireless systems often increases radio frequency interference (RFI) experienced by passive systems. This necessitates development of coexistence techniques and creation of new technology that enhances the existing and future wireless infrastructure. To study this problem, we are developing a unique testbed for collecting remote sensing datasets with ground truth in real-world settings, which will enable training, optimization, and benchmarking the coexistence solutions. The testbed includes (1) a software defined radio (SDR) based radiometer, incorporated with a dual-polarized microwave antenna operating in the L-band (1400 MHz–1427 MHz) and (2) prototyping SDR-based communication systems. This paper presents design and implementation of such radiometer from an unmanned aircraft system (UAS) for supporting different scenarios and geometries.more » « less
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Abstract The Consistent Artificial Intelligence (AI)-based Soil Moisture (CASM) dataset is a global, consistent, and long-term, remote sensing soil moisture (SM) dataset created using machine learning. It is based on the NASA Soil Moisture Active Passive (SMAP) satellite mission SM data and is aimed at extrapolating SMAP-like quality SM back in time using previous satellite microwave platforms. CASM represents SM in the top soil layer, and it is defined on a global 25 km EASE-2 grid and for 2002–2020 with a 3-day temporal resolution. The seasonal cycle is removed for the neural network training to ensure its skill is targeted at predicting SM extremes. CASM comparison to 367 global
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