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Cognitive Network Engineering Design Analytic Toolset (C-NEDAT)

Designing and Predicting Performance of Large-Scale, Multi-Tier Heterogeneous Networks

Network designers are increasingly called upon to deliver robust and adaptable communications for highly unpredictable environments, such as military battlefields and congested automotive roadways. The intense needs for robustness and adaptability in such networks have triggered research into cognitive networks that have the ability to ‘learn’ and generate real-time control actions to adapt to a wide diversity of requirements, resources and environments. However, the combination of diversity and ‘smart’ networking presents formidable problems in generating network designs that are sufficiently reliable and robust.

C-NEDAT:  Leveraging Network Science-Based Approaches

Applied Communication Sciences, is addressing these challenges through C-NEDAT, a Cognitive Network Engineering Design Analytic Toolset. C-NEDAT can be used to both design and analyze the performance of cognitive networks. Based on formal network science-based approaches, the C-NEDAT tool can:

a)  Synthesize (create) network designs/plans given information about missions that the networks are expected to support in terms of mission objectives/goals, constraints, requirements and resource availability; and,

b)  Analyze performance trade-offs between different network design/plan options that can be made available to the network deployment and management personal based on resource info, mission objectives, requirements and constraints. 

Other salient features of C-NEDAT include its abilities to:

  • Find ‘best’ design(s) based on mission objectives & constraints
  • Provide rapid what-if analysis and performance tradeoffs among different design options
  • Accommodate new design models and capabilities through its modular and extensible software architecture
  • Complement Discrete Event Simulation tools which provide verification and validation of a specific network design

C-NEDAT currently supports:

  • Thousands of mobile platforms
  • Cognitive and traditional radios, with multiple radios per platform
  • Diverse MANET ad hoc networks
  • Time sequencing of traffic and traffic types (voice/video/data/chat/SA)
  • Realistic data link interference models (based on TIREM model)
  • Diverse subnet, topology, routing, DSA & MAC protocols, with cognitive learning options

C-NEDAT is a US Army funded project.

Going Forward

Applied Communication Sciences researchers are working to extend the use of both off-line designs and in-line control/maintenance actions. These on-going investigations include the use of in-control actions for frequency and power management, routing, MAC and automating design actions.