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All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Communications and Intelligent Systems Division Overview

Provides expertise, and plans, conducts and directs research and engineering development in the competency fields of advanced communications and intelligent systems technologies for applications in current and future aeronautics and space systems.Advances communication systems engineering, development and analysis needed for Glenn Research Center's leadership in communications and intelligent systems technology. Focus areas include advanced high frequency devices, components, and antennas; optical communications, health monitoring and instrumentation; digital signal processing for communications and navigation, and cognitive radios; network architectures, protocols, standards and network-based applications; intelligent controls, dynamics and diagnostics; and smart micro- and nano-sensors and harsh environment electronics. Research and discipline engineering allow for the creation of innovative concepts and designs for aerospace communication systems with reduced size and weight, increased functionality and intelligence. Performs proof-of-concept studies and analyses to assess the impact of the new technologies.

Intelligent Systems↗
NASA NTRS2017

Assessment of Cognitive Communications Interest Areas for NASA Needs and Benefits

This effort provides a survey and assessment of various cognitive communications interest areas, including node-to-node link optimization, intelligent routing/networking, and learning algorithms, and is conducted primarily from the perspective of NASA space communications needs and benefits. Areas of consideration include optimization methods, learning algorithms, and candidate implementations/technologies. Assessments of current research efforts are provided with mention of areas for further investment. Other considerations, such as antenna technologies and cognitive radio platforms, are briefly provided as well.

Knoblock, Eric J.↗
NASA NTRS2019

Multi-Band and Multi-Function Reconfigurable Gallium Nitride Based Fully Solid-State Microwave Power Module for Cognitive Radio/Radar Platforms

This paper presents as a proof-of-concept (POC) the design, integration, and performance of a novel reconfigurable S-/X-band Gallium Nitride (GaN) based fully solid-state microwave power module (SSMPM) for the role as the transmit module in a cognitive radio (CR). The SSMPM synergistically integrates multiple amplifiers through diplexing and high power switches to enable a single SSMPM capable of functioning as both S-/X-band amplifiers for telemetry, tracking, and command (TT&C), telecommunications, and science data downlink or as X-band radar for proximity sensing onboard a planetary exploration spacecraft. Integration of an electric field shaping field plate (FP) onto the GaN high electron mobility transistors (HEMTs) in this SSMPM provides increased performance and reliability for operation in the harsh conditions of space. This SSMPM is capable of delivering saturated power (Psat) of 39 dBm (8 W continuous wave (CW)) at S-band, P(sub sat) of 43 dBm (20 W CW) at X-band, and P(sub sat) of >50 dBm (>100 W Pulsed) at X-band.

Simons, Rainee N.↗
NASA NTRS2021

A Low-Memory Spectral-Correlation Analyzer for Digital QAM-SRRC Waveforms

Cyclostationary signal processing (CSP) provides the ability to estimate received waveforms' statistical features blindly. Quadrature amplitude modulated (QAM) waveforms, when filtered by the square-root-raised cosine (SRRC) pulse shape function, have cyclic features that CSP can exploit to detect waveform parameters such as symbol rate (SR) and center frequency (CF). The estimation of these SR-CF pairs enables a cognitive radio (CR) to perform spectrum sensing techniques such as spectrum sharing and interference mitigation. Here, we investigate a field-programmable gate array (FPGA) application of a blind symbol rate-center frequency estimator. First, this study provides a background on the theory behind the cyclic spectral density function (CSD), spectral correlation analyzers (SCA), and spectrum sensing. Following this is a discussion on the motivation for CubeSat spectrum sensing. An SCA implementation for low-memory devices, such as FPGA-based CubeSat, is then describes. The paper concludes by reporting the performance characteristics of the newly developed streaming-based SCA.

FPGA↗
NASA NTRS2016

Exploring Cognition Using Software Defined Radios for NASA Missions

NASA missions typically operate using a communication infrastructure that requires significant schedule planning with limited flexibility when the needs of the mission change. Parameters such as modulation, coding scheme, frequency, and data rate are fixed for the life of the mission. This is due to antiquated hardware and software for both the space and ground assets and a very complex set of mission profiles. Automated techniques in place by commercial telecommunication companies are being explored by NASA to determine their usability by NASA to reduce cost and increase science return. Adding cognition the ability to learn from past decisions and adjust behavior is also being investigated. Software Defined Radios are an ideal way to implement cognitive concepts. Cognition can be considered in many different aspects of the communication system. Radio functions, such as frequency, modulation, data rate, coding and filters can be adjusted based on measurements of signal degradation. Data delivery mechanisms and route changes based on past successes and failures can be made to more efficiently deliver the data to the end user. Automated antenna pointing can be added to improve gain, coverage, or adjust the target. Scheduling improvements and automation to reduce the dependence on humans provide more flexible capabilities. The Cognitive Communications project, funded by the Space Communication and Navigation Program, is exploring these concepts and using the SCaN Testbed on board the International Space Station to implement them as they evolve. The SCaN Testbed contains three Software Defined Radios and a flight computer. These four computing platforms, along with a tracking antenna system and the supporting ground infrastructure, will be used to implement various concepts in a system similar to those used by missions. Multiple universities and SBIR companies are supporting this investigation. This paper will describe the cognitive system ideas under consideration and the plan for implementing them on platforms, including the SCaN Testbed. Discussions in the paper will include how these concepts might be used to reduce cost and improve the science return for NASA missions.

transmitters receivers↗
NASA NTRS2015

Reconfigurable, Cognitive Software-Defined Radio

Software-defined radio (SDR) technology allows radios to be reconfigured to perform different communication functions without using multiple radios to accomplish each task. Intelligent Automation, Inc., has developed SDR platforms that switch adaptively between different operation modes. The innovation works by modifying both transmit waveforms and receiver signal processing tasks. In Phase I of the project, the company developed SDR cognitive capabilities, including adaptive modulation and coding (AMC), automatic modulation recognition (AMR), and spectrum sensing. In Phase II, these capabilities were integrated into SDR platforms. The reconfigurable transceiver design employs high-speed field-programmable gate arrays, enabling multimode operation and scalable architecture. Designs are based on commercial off-the-shelf (COTS) components and are modular in nature, making it easier to upgrade individual components rather than redesigning the entire SDR platform as technology advances.

Bhat, Arvind↗
NASA NTRS2016

Implementation of a Parameterized Interacting Multiple Model Filter on an FPGA for Satellite Communications

In a communications channel, the space environment between a spacecraft and an Earth ground station can potentially cause the loss of a data link or at least degrade its performance due to atmospheric effects, shadowing, multipath, or other impairments. In adaptive and coded modulation, the signal power level at the receiver can be used in order to choose a modulation-coding technique that maximizes throughput while meeting bit error rate (BER) and other performance requirements. It is the goal of this research to implement a generalized interacting multiple model (IMM) filter based on Kalman filters for improved received power estimation on software-dened radio (SDR) technology for satellite communications applications. The IMM filter has been implemented in Verilog consisting of a customizable bank of Kalman filters for choosing between performance and resource utilization. Each Kalman filter can be implemented using either solely a Schur complement module (for high area efficiency) or with Schur complement, matrix multiplication, and matrix addition modules (for high performance). These modules were simulated and synthesized for the Virtex II platform on the JPL Radio Experimenter Development System (EDS) at NASA Glenn Research Center. The results for simulation, synthesis, and hardware testing are presented.

cognitive radio↗
NASA NTRSDate not supplied

Cognitive Tapered Slot Circular Array Antenna for Lunar Surface Communications

In this paper, we present the design of a cognitive tapered slot circular array (TSCA) antenna with multiple electronically switched sector beams for lunar surface communication. The circular array is capable of sensing the frequency, power, and direction of arrival of the RF signals. The design of the TSCA builds on our prior work on the development of a single TSA element with a balanced microstrip/coax feed. The measured return loss of the single TSA element shows wide bandwidth with good impedance match across the 5 to 35 GHz frequency range. Additionally, the single TSA has good radiation patterns. The single TSA serves as the building block for a four-element TSCA with multiple electronically switched sector beams for sensing and communications.

Antenna↗
NASA NTRS2015

Characterization of an In-Situ Ground Terminal via a Geostationary Satellite

In 2015, the Space Communications and Navigation (SCaN) Testbed project completed an S-Band ground station located at the NASA Glenn Research Center in Cleveland, Ohio. This S-Band ground station was developed to create a fully characterized and controllable dynamic link environment when testing novel communication techniques for Software Defined Radios and Cognitive Communication Systems. In order to provide a useful environment for potential experimenters, it was necessary to characterize various RF devices at both the component level in the laboratory and at the system level after integration. This paper will discuss some of the laboratory testing of the ground station components, with a particular focus/emphasis on the near-field measurements of the antenna. It will then describe the methodology for characterizing the installed ground station at the system level via a Tracking and Data Relay Satellite (TDRS), with specific focus given to the characterization of the ground station antenna pattern, where the max TDRS transmit power limited the validity of the non-noise floor received power data to the antenna main lobe region. Finally, the paper compares the results of each test as well as provides lessons learned from this type of testing methodology.

Ground Station↗
NASA NTRS2015

Characterization of an In-Situ Ground Terminal via a Geostationary Satellite

In 2015, the Space Communications and Navigation (SCaN) Testbed project completed an S-Band ground station located at the NASA Glenn Research Center in Cleveland, Ohio. This S-Band ground station was developed to create a fully characterized and controllable dynamic link environment when testing novel communication techniques for Software Defined Radios and Cognitive Communication Systems. In order to provide a useful environment for potential experimenters, it was necessary to characterize various RF devices at both the component level in the laboratory and at the system level after integration. This paper will discuss some of the laboratory testing of the ground station components, with a particular focus emphasis on the near-field measurements of the antenna. It will then describe the methodology for characterizing the installed ground station at the system level via a Tracking and Data Relay Satellite (TDRS), with specific focus given to the characterization of the ground station antenna pattern, where the max TDRS transmit power limited the validity of the non-noise floor received power data to the antenna main lobe region. Finally, the paper compares the results of each test as well as provides lessons learned from this type of testing methodology.

Radiation Pattern Analysis↗
NASA NTRS2022

Towards the Development of a Multi-Agent Cognitive Networking System for the Lunar Environment

This paper details the development of a multi-agent cognitive system intended to optimize networking performance in the lunar environment. One concept of the future of lunar communication, LunaNet, outlines a complex network of networks. Challenges such as scalability, interoperability, and reliability must first be addressed to successfully fulfill this vision. Machine intelligence can greatly reduce the reliance on human operators and enable efficient operations for tasks such as scheduling and network management. Machine learning, artificial intelligence, and other automated decision-making techniques can be used to allow network nodes to intelligently sense and adapt to changes in the environment such as link disruptions, new nodes joining the network, and support for a diverse range of protocols. Cognitive networking seeks to evolve these technologies into an autonomous system with improved science data return, reliability, and scalability. In this paper, we study four main areas as a means to further develop cognitive networking capabilities: networking protocol development, analysis of wireless data for modeling and simulation, development of algorithms for a multi-agent system, and spectrum sensing technology.

cognitive networking↗
NASA NTRS2019

The Space Communications and Navigation Testbed aboard International Space Station: Seven Years of Space-based Reconfigurable Software Defined Communications, Navigation, and Networking

The adoption of software defined radios offers space missions a new way to develop and operate space transceivers for communications and navigation.The SCaN Testbed on-board the ISS led groundbreaking efforts to champion use of software defined radios for space communications. The SCaN Testbed has allowed NASA, industry, academia, and international partners to pursue their respective interests in joint collaboration with NASA, and move this technology and it's applications to the space domain. Launched in 2012, The SCaN Testbed has logged over 4000 hours of operation exploring the development, reconfiguration, and operation of software defined radios and their software applications. Over the past seven years, experimenters and organizations from across the United States and around the world, have advanced the applications of software defined radios and networks using the SCaN Tested. Some of SCaN Testbed's successful experiments include the demonstration of the first Ka-band full duplex space transceiver, which became an R&D 100 award winning technology, and was inducted into the Space Technology Hall of Fame, following the launch and space deployment of a successful commercial product line based on the Testbed radios.Experiments have focused on new software development and operations concepts for understanding how to manage and apply this relatively new technology to space to improve communications and navigation for space science and exploration missions. The advanced capabilities of the software radios allow for multiple applications or functions (e.g., communication and navigation) to operate from the same radio platform. Multiple software waveform applications enable software component reuse and improve efficiency for multiple applications operating over different mission phases. The new capabilities of software defined radios such as on-orbit reconfiguration, also present new challenges such as increased operational complexity. Experiments of the SCaN testbed include more intelligent or cognitive applications to improve communications efficiency and manage the complexity of the radios, the communication channels, and the network itself. The software defined radios on the SCaN Testbed are each compliant to NASA's Space Telecommunications Radio System (STRS) Architecture. The STRS Architecture provides commonality among radio developments from different providers and different mission applications, and is designed to reduce the cost, risk, and complexity of unique and custom radio developments. This radio architecture standard defines common waveform software interfaces, methods of instantiation, operation, and documentation. As the SCaN Testbed concludes its operations on ISS, this presentation explores the advancements and accomplishments made to advance software defined radio technology and its applications for exploration. The accomplishments cover a number of experiment areas in Ka-band and S-band communications with TDRS, high rate communications, adaptive waveform operation, navigation using both GPA and Galileo constellations, complex networking and disruptive tolerant link protocols, user initiative service, and initial experiments with intelligent and cognitive applications which demonstrate the significant potential of software defined and cognitive radios.

Mortensen, Dale↗
NASA NTRS2015

An Overview of SBIR Phase 2 Communications Technology and Development

Technological innovation is the overall focus of NASA's Small Business Innovation Research (SBIR) program. The program invests in the development of innovative concepts and technologies to help NASA's mission directorates address critical research and development needs for agency projects. This report highlights innovative SBIR Phase II projects from 2007-2012 specifically addressing areas in Communications Technology and Development which is one of six core competencies at NASA Glenn Research Center. There are eighteen technologies featured with emphasis on a wide spectrum of applications such as with a security-enhanced autonomous network management, secure communications using on-demand single photons, cognitive software-defined radio, spacesuit audio systems, multiband photonic phased-array antenna, and much more. Each article in this booklet describes an innovation, technical objective, and highlights NASA commercial and industrial applications. This report serves as an opportunity for NASA personnel including engineers, researchers, and program managers to learn of NASA SBIR's capabilities that might be crosscutting into this technology area. As the result, it would cause collaborations and partnerships between the small companies and NASA Programs and Projects resulting in benefit to both SBIR companies and NASA.

secure communications software defined radio anten↗
NASA NTRS2017

Multi-Objective Reinforcement Learning-Based Deep Neural Networks for Cognitive Space Communications

Future communication subsystems of space exploration missions can potentially benefit from software-defined radios (SDRs) controlled by machine learning algorithms. In this paper, we propose a novel hybrid radio resource allocation management control algorithm that integrates multi-objective reinforcement learning and deep artificial neural networks. The objective is to efficiently manage communications system resources by monitoring performance functions with common dependent variables that result in conflicting goals. The uncertainty in the performance of thousands of different possible combinations of radio parameters makes the trade-off between exploration and exploitation in reinforcement learning (RL) much more challenging for future critical space-based missions. Thus, the system should spend as little time as possible on exploring actions, and whenever it explores an action, it should perform at acceptable levels most of the time. The proposed approach enables on-line learning by interactions with the environment and restricts poor resource allocation performance through virtual environment exploration. Improvements in the multiobjective performance can be achieved via transmitter parameter adaptation on a packet-basis, with poorly predicted performance promptly resulting in rejected decisions. Simulations presented in this work considered the DVB-S2 standard adaptive transmitter parameters and additional ones expected to be present in future adaptive radio systems. Performance results are provided by analysis of the proposed hybrid algorithm when operating across a satellite communication channel from Earth to GEO orbit during clear sky conditions. The proposed approach constitutes part of the core cognitive engine proof-of-concept to be delivered to the NASA Glenn Research Center SCaN Testbed located onboard the International Space Station.

space archtiecture↗
NASA NTRS2017

Multi-Objective Reinforcement Learning-based Deep Neural Networks for Cognitive Space Communications

Future communication subsystems of space exploration missions can potentially benefit from software-defined radios (SDRs) controlled by machine learning algorithms. In this paper, we propose a novel hybrid radio resource allocation management control algorithm that integrates multi-objective reinforcement learning and deep artificial neural networks. The objective is to efficiently manage communications system resources by monitoring performance functions with common dependent variables that result in conflicting goals. The uncertainty in the performance of thousands of different possible combinations of radio parameters makes the trade-off between exploration and exploitation in reinforcement learning (RL) much more challenging for future critical space-based missions. Thus, the system should spend as little time as possible on exploring actions, and whenever it explores an action, it should perform at acceptable levels most of the time. The proposed approach enables on-line learning by interactions with the environment and restricts poor resource allocation performance through virtual environment exploration. Improvements in the multiobjective performance can be achieved via transmitter parameter adaptation on a packet-basis, with poorly predicted performance promptly resulting in rejected decisions. Simulations presented in this work considered the DVB-S2 standard adaptive transmitter parameters and additional ones expected to be present in future adaptive radio systems. Performance results are provided by analysis of the proposed hybrid algorithm when operating across a satellite communication channel from Earth to GEO orbit during clear sky conditions. The proposed approach constitutes part of the core cognitive engine proof-of-concept to be delivered to the NASA Glenn Research Center SCaN Testbed located onboard the International Space Station.

space archtiecture↗
NASA NTRS2014

Space-Based Reconfigurable Software Defined Radio Test Bed Aboard International Space Station

The National Aeronautical and Space Administration (NASA) recently launched a new software defined radio research test bed to the International Space Station. The test bed, sponsored by the Space Communications and Navigation (SCaN) Office within NASA is referred to as the SCaN Testbed. The SCaN Testbed is a highly capable communications system, composed of three software defined radios, integrated into a flight system, and mounted to the truss of the International Space Station. Software defined radios offer the future promise of in-flight reconfigurability, autonomy, and eventually cognitive operation. The adoption of software defined radios offers space missions a new way to develop and operate space transceivers for communications and navigation. Reconfigurable or software defined radios with communications and navigation functions implemented in software or VHDL (Very High Speed Hardware Description Language) provide the capability to change the functionality of the radio during development or after launch. The ability to change the operating characteristics of a radio through software once deployed to space offers the flexibility to adapt to new science opportunities, recover from anomalies within the science payload or communication system, and potentially reduce development cost and risk by adapting generic space platforms to meet specific mission requirements. The software defined radios on the SCaN Testbed are each compliant to NASA's Space Telecommunications Radio System (STRS) Architecture. The STRS Architecture is an open, non-proprietary architecture that defines interfaces for the connections between radio components. It provides an operating environment to abstract the communication waveform application from the underlying platform specific hardware such as digital-to-analog converters, analog-to-digital converters, oscillators, RF attenuators, automatic gain control circuits, FPGAs, general-purpose processors, etc. and the interconnections among different radio components.

Communications↗
NASA NTRS2017

Implementation of a Space Communications Cognitive Engine

Although communications-based cognitive engines have been proposed, very few have been implemented in a full system, especially in a space communications system. In this paper, we detail the implementation of a multi-objective reinforcement-learning algorithm and deep artificial neural networks for the use as a radio-resource-allocation controller. The modular software architecture presented encourages re-use and easy modification for trying different algorithms. Various trade studies involved with the system implementation and integration are discussed. These include the choice of software libraries that provide platform flexibility and promote reusability, choices regarding the deployment of this cognitive engine within a system architecture using the DVB-S2 standard and commercial hardware, and constraints placed on the cognitive engine caused by real-world radio constraints. The implemented radio-resource allocation-management controller was then integrated with the larger spaceground system developed by NASA Glenn Research Center (GRC).

reinforcement learning↗
NASA NTRS2017

Implementation of a Space Communications Cognitive Engine

Although communications-based cognitive engines have been proposed, very few have been implemented in a full system, especially in a space communications system. In this paper, we detail the implementation of a multi-objective reinforcement-learning algorithm and deep artificial neural networks for the use as a radio-resource-allocation controller. The modular software architecture presented encourages re-use and easy modification for trying different algorithms. Various trade studies involved with the system implementation and integration are discussed. These include the choice of software libraries that provide platform flexibility and promote reusability, choices regarding the deployment of this cognitive engine within a system architecture using the DVB-S2 standard and commercial hardware, and constraints placed on the cognitive engine caused by real-world radio constraints. The implemented radio-resource allocation-management controller was then integrated with the larger spaceground system developed by NASA Glenn Research Center (GRC).

reinforcement learning↗