Link capability driven network planning and operation
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communication network efficiency resource allocati↗
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Next generation communications satellites: multiple access and network studies
Efficient resource allocation and network design for satellite systems serving heterogeneous user populations with large numbers of small direct-to-user Earth stations are discussed. Focus is on TDMA systems involving a high degree of frequency reuse by means of satellite-switched multiple beams (SSMB) with varying degrees of onboard processing. Algorithms for the efficient utilization of the satellite resources were developed. The effect of skewed traffic, overlapping beams and batched arrivals in packet-switched SSMB systems, integration of stream and bursty traffic, and optimal circuit scheduling in SSMB systems: performance bounds and computational complexity are discussed.
Meadows, H. E.↗
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↗
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↗
Space Communications and Navigation Validation: Extracting Data for the Strategic Center for Networking, Integration, and Communications Scheduling Algorithms
Efficiency in communication system architecture performance between Space Communications and Navigation (SCaN) assets and missions is crucial, as space communication is varied, complex, and often not utilized to its full potential. The SCaN Strategic Center for Networking, Integration, and Communications (SCENIC) new scheduling algorithms, which are designed to simulate the allocation of resources between SCaN assets and missions, have the potential to simulate an increase of this efficiency; however, they require real-world data to be validated against. The purpose of this project was to extract said validation data, which details the frequency and duration of utilized contact windows between missions and assets in the Near Earth Network (NEN), Space Network (SN), and Deep Space Network (DSN). Stored as images in daily operations summaries (DOSs), the tabular data existed in a variety of file formats such as.pdf, .docx, and .doc. Since the tables were stored as images, ABBYY® FineReader® (ABBYY Software Ltd.) optical character recognition (OCR) was implemented, which is a proprietary software that reads images from text. The comma separated value (CSV) output was utilized as input to a series of MATLAB® (The MathWorks, Inc.) methods for reformatting, at which point it was ready to be machine-read. Finally, the results were converted to a Microsoft Excel format for human readability. Along with being used for validation purposes, the data will also be used to map equipment degradation as a function of time to analyze the reliability of network assets.
Kontur, Noah P.↗
Exploring New Frontiers in Space Communications: Enhancing Delay Tolerant Networking through Cloud and Containerization
The High-rate Delay Tolerant Networking (HDTN) project at NASA Glenn Research Center has developed software that enables more flexible, reliable, and efficient space internetworking by using modern computing techniques such as cloud services, microservices, network function virtualization, software defined networking, and a distributed architecture. HDTN is built upon the Bundle Protocol and related convergence layers which have been developed to mitigate the challenges of the space networking environment including long delays, asymmetric data rates, and intermittent connectivity. The HDTN implementation employs asynchronous message processing tasks which allow for non-blocking operations as well as deployment in both centralized and distributed architectures. This paper investigates deploying HDTN in a containerized approach on the NASA Goddard’s Mission Cloud Platform using Amazon Web Services Elastic Compute Cloud (EC2). Commercial cloud computing will lower operating costs, provide flexible resource allocation, and allow for interconnectivity between multiple NASA centers as well as external partners. Containerization using Docker will enable greater portability and scalability for HDTN to be deployed into a variety of environments. We discuss possible NASA missions and use-cases such as the Laser Communications Relay Demonstration (LCRD) where the services provided by HDTN (reliable transport, high-rate message processing, and store-and-forward capabilities) will be enhanced through cloud computing and containerization. In addition, we describe the HDTN architecture and possible microservice-based networking approaches that can be obtained via HDTN’s configuration capabilities. Finally, we detail the EC2 specifications needed to achieve data rates greater than 1 Gbps to support optical communication missions such as LCRD.
Blake LaFuente↗
Recent results in Mars Relay Network planning and scheduing
At different time periods in the future, Mars missions will overlap and previous studies indicate that during such periods existing deep space communication infrastructure will not be able to handle all Mars communication needs. A plausible solution is to perform optimal resource allocation for the Mars relay communication network; a network consisting of multiple surface units and orbiters on Mars and the Deep Space Stations. Unlike direct-to-earth, a relay communication, either in real-time or store-and-forward, can increase network science data return, reduce surface unit's direct-to-earth communication demands, and enable communication even when the surface unit is not facing Earth. It is the objective of this paper to take advantage of the relay operation to efficiently plan and schedule the network communications.
Relay Network planning and scheduling constrained ↗