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Results for “software-defined networking”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Initial Results of the Software-Driven Navigation for Station Experiment

To enable the next generation of robotic and human exploration of the solar system, improvements are needed to enable robust and accurate autonomous navigation. The purpose of this work is to take advantage of the growth in and use of software-defined platforms to incorporate additional navigation capability on existing assets, while also incorporating with new vehicle designs. The Software-driven Navigation for Station Experiment focuses on implementing two soft solutions to this: transmitting pseudolite signals to perform ranging and Doppler measurements as part of the signal coding (similar to underlying Global Navigation Satellite System approaches), and the Multi-spacecraft Autonomous Positioning System, which uses existing communication protocols to embed navigation and timing information to be shared among all assets in a peer-to-peer network. These technologies were implemented on the SCaN Testbed onboard the International Space Station and exercised over the course of mid-June and late-July 2018. This paper will discuss the operational architecture, experiment plan, and initial results from the data collected. One of the key conclusions of this work is the strong need for stable accurate clock synchronization across the dispersed space network.

Anzalone, Evan J.↗
NASA NTRS2020

On-Orbit Validation of a Framework for Spacecraft-Initiated Communication Service Requests with NASA's SCaN Testbed

We design, analyze, and experimentally validate a framework for demand-based allocation of high-performance space communication service in which the user spacecraft itself initiates a request for service. Leveraging machine-to-machine communications, the automated process has potential to improve the responsiveness and efficiency of space network operations. We propose an augmented ground station architecture in which a hemispherical-pattern antenna allows for reception of service requests sent from any user spacecraft within view. A suite of ground-based automation software acts upon these direct-to-Earth requests and allocates access to high-performance service through a ground station or relay satellite in response to immediate user demand. A software-defined radio transceiver, optimized for reception of weak signals from the helical antenna, is presented. Design and testing of signal processing equipment and a software framework to handle service requests is discussed. Preliminary results from on-orbit demonstrations with a testbed onboard the International Space Station are presented to verify feasibility of the concept.

Adam M Gannon↗
NASA NTRSDate not supplied

Cloud-Based Demodulation and Data Distribution of a Satellite Downlink

Ground station networks connected to the cloud allow space missions to have global communications coverage without operating their own infrastructure. In this work, we describe the communications architecture for the TechEdSat-13 mission, which performed the first in-space characterization of a neuromorphic processor. The mission utilizes a commercial provider for S-band downlinks. A suite of cloud services and open-source software such as GNU Radio are leveraged to demodulate signals received by an AWS ground station during passes with TechEdSat-13 and store recovered data. Once a pass is scheduled, the entire process takes place without human intervention. On-orbit results the past year of operations are presented, demonstrating the advantages of this approach over traditional operator-owned ground stations. Use of software-defined radio makes possible custom signal processing. The homogeneity of apertures and their interfaces to the cloud simplifies scaling across many sites. This abundance of candidate links lays the groundwork for intelligent scheduling agents to optimize pass selection across several factors, automatically recover from failed contacts, and gather metrics to learn from past performance.

cloud demodulation↗