Modeling and evaluation of aerial layer communications system architectures
Author(s)
Ajemian, Stephen P
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Other Contributors
System Design and Management Program.
Advisor
Bruce Cameron.
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Airborne networks are being developed to provide communications services in order to augment space-based and terrestrial communications systems. These airborne networks must provide point to point wireless communications capabilities between aircraft and to ground-based users. Architecting airborne networks requires evaluating the capabilities offered by candidate aircraft to operate at the required altitudes to bridge communications among ground users dispersed over large geographic areas. Decision makers are often faced with choices regarding the type and number of aircraft to utilize in an airborne network to meet information exchange requirements. In addition, the type of radio required to meet user needs may also factor into the architecture evaluation for an airborne network. Aircraft and radio design choices must be made under cost constraints in order to deliver capable communications architectures at an acceptable cost. Evaluating communications architectures is often conducted with modeling and simulation. However, evaluations typically focus on specific network configurations and can become intractable when varying design variables such as aircraft and radio types due to the complexity of the trade space being analyzed. Furthermore, the growth in choices for design variables (such as additional aircraft types) can lead to enormous growth in the number of feasible candidate architectures to analyze. The methodology developed and presented herein describes an approach for evaluating a large number of architecture combinations which vary on aircraft type and radio type for representative airborne networks. The methodology utilizes modeling and simulation to generate wireless communications performance data for candidate aircraft and radio types and enumerates a large trade space through a computational tool. The trade space is then evaluated against a multi-objective decision model to rapidly down-select to a handful of candidate architectures for more detailed analysis. The results of this analysis provide effective tools for reducing the complex trade space to a tractable number of architectures to make an informed architectural decision with no prior articulation of preferences for performance measures. For the notional concept of operation analyzed, the number of feasible architectures was approximately 500,000 for each of the two radio types examined. The decision model implemented reduced the feasible architectures to approximately 50 near-optimal architectures for each radio type. From this manageable set of near-optimal architectures, an analysis is conducted to evaluate marginal benefits versus cost to further reduce the candidate architectures to 3 architectures for each radio type. From these remaining architectures, detailed analysis and visualization can be conducted to aid decision makers in articulating preferences and identifying a single "best" architecture based on mission needs. The enumeration of the trade space using the computational tool and multi-objective decision model is highly flexible to incorporating new constraints and generating new candidate architectures as stakeholder preferences become clearer. The trade space enumeration and decision model can be conducted rapidly to down-select large trade spaces to a tractable number of communications architectures to inform an architectural recommendation.
Description
Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, Engineering Systems Division, System Design and Management Program, 2014. Cataloged from PDF version of thesis. Includes bibliographical references (pages 90-91).
Date issued
2014Department
System Design and Management Program.; Massachusetts Institute of Technology. Engineering Systems DivisionPublisher
Massachusetts Institute of Technology
Keywords
Engineering Systems Division., System Design and Management Program.