The growing complexity of spacecraft constellations, communication relay offerings, and mission architectures drives the need for the development of autonomous communication systems. The National Aeronautics and Space Administration (NASA) has traditionally launched single spacecraft missions that are served by the Space Communication and Navigation (SCaN) program. Operations on SCaN networks are typically scheduled weeks in advance, and often each asset serves a single user spacecraft at a time. Recent movement towards swarm missions could make the current approach unsustainable. Additionally, the integration of commercial communication service providers will substantially increase the data transfer options available to new missions. NASA science missions have found benefit in launching swarms of space-craft, allowing coordinated simultaneous observations from different perspectives. Inter-spacecraft communication (mesh networking) is an enabler for this architecture, as are CubeSats that allow cost-effective provisioning of distributed mission assets. As more complex swarm missions launch, one challenge is coordinating communication within the swarm and choosing the appropriate mechanism for telemetry, tracking, control, and data services to and from Earth. Cognitive communications research conducted by SCaN aims to mitigate the increasing communication complexity for mission users by increasing the autonomy of links, networks, and service scheduling. By considering automation techniques including recent advances in artificial intelligence and machine learning, cognitive algorithms and related approaches enable increased mission science return, improved resource utilization for service provider networks, and resiliency in unpredictable or unplanned environments. The Cognitive Communications Project at the NASA Glenn Research Center develops applications of data-driven, nondeterministic methods to improve the autonomy of space communication. The project emphasizes the development of decentralized space networks with artificial intelligence agents optimizing communication link throughput, data routing, and system-wide asset management. This chapter discusses the objectives, approaches, and opportunities of the research to address growing needs of the space communications community.

Chapter Contents:

• 16.1 Introduction
• 16.2 Defining cognition
• 16.3 Focus areas
• 16.4 Cognitive links
• 16.4.1 Radio frequency interference mitigation
• 16.4.2 Radio link optimization
• 16.4.3 Automatic receiver configuration
• 16.4.4 Deep learning communication links
• 16.5 Cognitive networks
• 16.5.1 Delay-tolerant networking
• 16.5.2 Intelligence in the DTN architecture
• 16.5.3 Cognition in the DTN protocols
• 16.5.4 Legacy, infrastructure, and bootstrapping intelligence
• 16.5.5 Virtualization in future cognitive networks
• 16.6 Cognitive systems
• 16.6.1 User-initiated service
• 16.6.2 System-wide intelligence
• 16.7 Enabling technology
• 16.7.1 Reconfigurable hardware
• 16.7.2 Cognitive processing challenges
• 16.8 Conclusion
• References

Inspec keywords: telecommunication computing; satellite links; communication complexity; radiotelemetry; cognitive radio; space communication links; aircraft communication; wireless mesh networks; space vehicles; learning (artificial intelligence)

Other keywords: telemetry; mesh networking; Cognitive Communications Project; data routing; autonomous communication systems; Space Communication and Navigation program; SCaN program; communication relay offerings; space communication community; system-wide asset management; spacecraft constellation complexity; complex swarm missions; NASA Glenn Research Center; National Aeronautics and Space Administration; mission architectures; NASA space systems

Subjects: Satellite communication systems; Computational complexity; Telemetry; Space communication systems; Knowledge engineering techniques; Mobile radio systems; Communications computing