Data return capabilities of the deep space network in the 1970s.
Deep Space Network /DSN/ with ground communications network, centralized control and data processing, discussing future data return and control capabilities
DATA ACQUISITION↗
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Space programs summary 37-50. Volume 2 - The Deep Space Network Bimonthly report, 1 Jan. - 29 Feb. 1968
Summary information on technical activities of deep space network, space flight projects, communication systems, and deep space instrumentation facilities
Source record↗
The Network Information Management System (NIMS) in the Deep Space Network
In an effort to better manage enormous amounts of administrative, engineering, and management data that is distributed worldwide, a study was conducted which identified the need for a network support system. The Network Information Management System (NIMS) will provide the Deep Space Network with the tools to provide an easily accessible source of valid information to support management activities and provide a more cost-effective method of acquiring, maintaining, and retrieval data.
Wales, K. J.↗
DSN capabilities and facilities. The deep space network
Ground communication system, deep space network and facilities, instrumentation, and space flight operations and facility for NASA program
SPACE EXPLORATION↗
The Deep Space Network. An instrument for radio navigation of deep space probes
The Deep Space Network (DSN) network configurations used to generate the navigation observables and the basic process of deep space spacecraft navigation, from data generation through flight path determination and correction are described. Special emphasis is placed on the DSN Systems which generate the navigation data: the DSN Tracking and VLBI Systems. In addition, auxiliary navigational support functions are described.
Renzetti, N. A.↗
Space Network Control Conference on Resource Allocation Concepts and Approaches
The results are presented of the Space Network Control (SNC) Conference. In the late 1990s, when the Advanced Tracking and Data Relay Satellite System is operational, Space Network communication services will be supported and controlled by the SNC. The goals of the conference were to survey existing resource allocation concepts and approaches, to identify solutions applicable to the Space Network, and to identify avenues of study in support of the SNC development. The conference was divided into three sessions: (1) Concepts for Space Network Allocation; (2) SNC and User Payload Operations Control Center (POCC) Human-Computer Interface Concepts; and (3) Resource Allocation Tools, Technology, and Algorithms. Key recommendations addressed approaches to achieving higher levels of automation in the scheduling process.
Moe, Karen L.↗
Developing High Performance Space Networking Capabilities for the International Space Station and Beyond
A performance optimized implementation of Delay Tolerant Networking (DTN) with the capacity of gigabit-per second rates is developed for the International Space Station (ISS) and missions demanding large amounts of communications bandwidth. An overview of the High-rate Delay Tolerant Networking (HDTN) architecture and support for different convergence layers is provided. This paper then presents an overview of the testing and integration efforts to evaluate interoperability and capability in relevant environments. The first was interoperability testing with DTN Marshall Enterprise (DTNME) which resulted in near-gigabit per second data rates. This was followed by ISS emulation testing with the Software Development and Integration Laboratory (SDIL) at the Lyndon B. Johnson Space Center (JSC) and local testing based on the ISS DTN network topology. The local tests resulted in the discovery of potential sources of performance loss in the network and demonstrated near-gigabit rates between HDTN and DTNME.
Daniel Raible↗
Future Mission Trends and their Implications for the Deep Space Network
Planning for the upgrade and/or replacement of Deep Space Network (DSN) assets that typically operate for forty or more years necessitates understanding potential customer needs as far into the future as possible. This paper describes the methodology Deep Space Network (DSN) planners use to develop this understanding, some key future mission trends that have emerged from application of this methodology, and the implications of the trends for the DSN's future evolution. For NASA's current plans out to 2030, these trends suggest the need to accommodate: three times as many communication links, downlink rates two orders of magnitude greater than today's, uplink rates some four orders of magnitude greater, and end-to-end link difficulties two-to-three orders of magnitude greater. To meet these challenges, both DSN capacity and capability will need to increase.
future mission needs↗
DVB-S2 Experiment over NASA's Space Network
The commercial DVB-S2 standard was successfully demonstrated over NASAs Space Network (SN) and the Tracking Data and Relay Satellite System (TDRSS) during testing conducted September 20-22nd, 2016. This test was a joint effort between NASA Glenn Research Center (GRC) and Goddard Space Flight Center (GSFC) to evaluate the performance of DVB-S2 as an alternative to traditional NASA SN waveforms. Two distinct sets of tests were conducted: one was sourced from the Space Communication and Navigation (SCaN) Testbed, an external payload on the International Space Station, and the other was sourced from GRCs S-band ground station to emulate a Space Network user through TDRSS. In both cases, a commercial off-the-shelf (COTS) receiver made by Newtec was used to receive the signal at White Sands Complex. Using SCaN Testbed, peak data rates of 5.7 Mbps were demonstrated. Peak data rates of 33 Mbps were demonstrated over the GRC S-band ground station through a 10MHz channel over TDRSS, using 32-amplitude phase shift keying (APSK) and a rate 89 low density parity check (LDPC) code. Advanced features of the DVB-S2 standard were evaluated, including variable and adaptive coding and modulation (VCMACM), as well as an adaptive digital pre-distortion (DPD) algorithm. These features provided additional data throughput and increased link performance reliability. This testing has shown that commercial standards are a viable, low-cost alternative for future Space Network users.
waveforms↗
Spacecraft Block Scheduling for NASA’s Deep Space Network
Currently, NASA’s Deep Space Network (DSN) is responsible for uplink to, downlink from, and/or tracking of dozens of missions for space agencies across the world. The DSN scheduling process starts about four months prior to the start of the schedule week, a process in which requirements are defined and then the schedule is created, de-conflicted, and negotiated over the next 2–3 weeks with a team of mission representatives. Now scheduled for late 2019, Exploration Mission1 (EM-1) will deploy upwards of 12 SmallSat missions that will be served by the DSN. This will increase the DSN’s actively serviced spacecraft by up to 30%, further increasing the difficulty of meeting all mission needs via the over subscribed network. To mitigate their impact on DSN scheduling, a block scheduling process is proposed for scheduling the SmallSats. Block scheduling consists of aggregating spacecraft together into larger “pseudo-spacecraft” based on geometric alignment that then follow the same process as any other DSN mission to receive segments of track time. These tracks are then decomposed into tracks for individual users based on their specific requirements. This paper describes a full novel scheduling tool set for building candidate blocks, evaluating the efficacy of these blocks, and optimal and suboptimal de-blocking schemes. To demonstrate these developed tools, results from three simulations are presented: a blocking example with lunar SmallSats, blocking potential in the greater DSN spacecraft catalog, and opportunistic multiple spacecraft per aperture potential for the DSN spacecraft catalog. Block scheduling has the potential to reduce overhead and scheduling resources for the EM-1 SmallSats while also providing them with a better means to meet their mission requirements.
Bilén, Sven G.↗
Large-Scale Space Network Simulator for Performance-Optimized DTNs
High-rate Delay Tolerant Networking (HDTN) is a performance-optimized Delay Tolerant Networking (DTN) implementation that can provide multigigabit per second data rates in disruptive and high-latency space networks. Routing, especially in large-scale space networks, remains challenging due to network topologies that evolve over time. This paper presents a simulation tool that enables HDTN implementation testing at accelerated speeds, which is key for routing in large-scale space networks.
High data-rate Delay Tolerant Networking↗
Space mission support by NASA Space Network
Normal NASA operational considerations and Phase 1 of Space Network (SN) interoperability foresees S band cross support between user spacecraft and space networks or partnering agencies. A typical interoperable scenario for an S band user spacecraft of a partner space agency with the NASA SN is presented. ESA's Hermes Manned Spaceplane exemplifies a typical interoperable user. In order to demonstrate the support potential of the NASA SN, the internal and external interfaces of the TDRSS (Tracking and Data Relay Satellite System), ATDRSS (Advanced TDRSS), and supporting elements are examined. Communications and operational requirements for a typical mission supported by the NASA SN are discussed.
Godfrey, Robert↗
The Deep Space Network
Progress on the Deep Space Network (DSN) supporting research and technology, advanced development, engineering and implementation, and DSN operations is presented. The functions and facilities of the DSN are described.
Source record↗
Interference susceptibility of a typical deep space network receiving system
The Deep Space Network (DSN), operated by the Jet Propulsion Laboratory for NASA, is used primarily for communication with interplanetary spacecraft. The high sensitivity required to achieve communication with distant spacecraft makes the DSN very susceptible to radio frequency interference (RFI). In this article a comprehensive description of the interference susceptibility of a typical DSN receiving system is given. Specifically, the effects of interference on the carrier tracking loop, the telemetry receiving subsystem, and the saturation effect on the maser, are presented.
Sue, M. K.↗
XML Technology Enables Redesigned Deep Space Network Operations
NASA's Deep Space Network (DSN) provides telecommunication support for all NASA missions.
XML application integration DSN operations↗
The Deep Space Network large array
The Deep Space Network (DSN) is primarily used for telecommunications with scientific spacecraft engaged in solar system exploration.
DSN↗
An Analysis of Database Replication Technologies with Regard to Deep Space Network Application Requirements
The Deep Space Network (DSN) has three communication facilities which handle telemetry, commands, and other data relating to spacecraft missions. The network requires these three sites to share data with each other and with the Jet Propulsion Laboratory for processing and distribution. Many database management systems have replication capabilities built in, which means that data updates made at one location will be automatically propagated to other locations. This project examines multiple replication solutions, looking for stability, automation, flexibility, performance, and cost. After comparing these features, Oracle Streams is chosen for closer analysis. Two Streams environments are configured - one with a Master/Slave architecture, in which a single server is the source for all data updates, and the second with a Multi-Master architecture, in which updates originating from any of the servers will be propagated to all of the others. These environments are tested for data type support, conflict resolution, performance, changes to the data structure, and behavior during and after network or server outages. Through this experimentation, it is determined which requirements of the DSN can be met by Oracle Streams and which cannot.
Oracle Database File System↗
AI and Autonomy Initiatives for NASA’s Deep Space Network (DSN)
NASA’s Deep Space Network (DSN) consists of thirteen large (34- and 70-meter) antennas that are used to communicate with approximately 40 NASA and partner spacecraft, all at great distance from the earth (generally at Lunar distances and beyond). The DSN has a long history — over 50 years — and has evolved with cutting edge, often custom, telecommunications equipment and associated software systems. In recent years, and in preparation for an increasing future demand, there has been an effort to invest in initiatives that will result in significant cost savings in the future. These efforts are building on, or augmenting, the recent deployment of “Follow-the-Sun” operations (day shift remote operational control of the entire network from each of the three antenna complexes in turn) — which is being deployed in 2017. This paper focuses on Adaptive Demand Access: in a paradigm shift from completely pre-planned operations, this concept calls for spacecraft to signal their intent (or not) for near-future contacts, in case they have science results of interest, or have experienced an anomaly. This would take advantage of a beacon tone transmission, which can be detected using smaller antennas. When a connection request is received, the DSN ground systems would adaptively accommodate the request, inserting the contact into the plan as soon as possible, subject to constraints and priorities. The demand access concept incorporates onboard data analysis and science data processing, so that beacon tones can be generated with maximum information. This area is representative of several where infusing AI technologies can lead to improved effectiveness of the DSN as the network readies for support of expanded Mars exploration efforts in the 2020’s and beyond.
Wyatt, E. Jay↗