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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. 

The Third Generation Partnership Project (3GPP) introduced the fifth generation new radio (5G NR) specifications which offer much higher flexibility than legacy cellular communications standards to better handle the heterogeneous service and performance requirements of the emerging use cases. This flexibility, however, makes the resources management more complex. This paper therefore designs a data driven resource allocation method based on the deep Q-network (DQN). The objective of the proposed model is to maximize the 5G NR cell throughput while providing a fair resource allocation across all users. Numerical results using a 3GPP compliant 5G NR simulator demonstrate that the DQN scheduler better balances the cell throughput and user fairness than existing schedulers. 

The main resource for providing wireless services is radio frequency (RF) spectrum. In order to explore new uses of spectrum shared among radio systems and services, field data needs to be collected. In this paper we design a testbed that can generate different 5G New Radio (NR) downlink transmission frames using the MATLAB 5G Toolbox, software-defined radio (SDR) hardware and GNU Radio Companion. This system will be used as a part of a testbed to study the RF interference caused by 5G transmissions to remote sensing receivers and evaluate different mechanisms for co-channel coexistence. 

Receiver nonlinearity gives rise to intermodulation products that are caused by two strong adjacent channel signals called blockers. The nonlinear distortion effects are significantly higher for multiple antenna wideband systems in dispersive environments because third order intermodulation products decreases the signal-to-noise ratio (SNR) at the output of the equalization process. This complicates the demodulation process and increases the bit error rate. This paper considers such nonlinear distortion in the context of space-time shift keying (STSK)-enabled wideband single-carrier systems and proposes an iterative space-time block equalization (ISTBE) framework for frequency domain equalization. We present our design of a practical ISTBE receiver based on the turbo principle and numerically demonstrate that it effectively removes the residual inter-symbol interference while suppressing high-power blockers and the in-band intermodulation distortion that they cause. The proposed system is thus suitable for simple wideband radio frequency front ends operating in the weak nonlinear region and enables adjacent channel spectrum coexistence with heterogeneous transmitters and receivers of different qualities. 

Research has shown that communications systems and receivers suffer from high power adjacent channel signals, called blockers, that drive the radio frequency (RF) front end into nonlinear operation. Since simple systems, such as the Internet of Things (IoT), will coexist with sophisticated communications transceivers, radars and other spectrum consumers, these need to be protected employing a simple, yet adaptive solution to RF nonlinearity. This paper therefore proposes a flexible data driven approach that uses a simple artificial neural network (ANN) to aid in the removal of the third order intermodulation distortion (IMD) as part of the demodulation process. We introduce and numerically evaluate two artificial intelligence (AI)-enhanced receivers—ANN as the IMD canceler and ANN as the demodulator. Our results show that a simple ANN structure can significantly improve the bit error rate (BER) performance of nonlinear receivers with strong blockers and that the ANN architecture and configuration depends mainly on the RF front end characteristics, such as the third order intercept point (IP3). We therefore recommend that receivers have hardware tags and ways to monitor those over time so that the AI and software radio processing stack can be effectively customized and automatically updated to deal with changing operating conditions. 

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.