Robust Resource Allocation in an Air Operational Model and Robust Tasking of Wireless Airborne Networks

Abstract

The focus of our effort is to study issues regarding robust resource allocation in air operations and robust tasking in wireless airborne networks. For the robust resource allocation, model uncertainties are considered in the generation of a state-based control policy at the strategic level in an air operation. A generic model is used to explain the aspects of modeling, control design, and implementation. Uncertainties are introduced into the transition-rates of the strategic model. A robust and constrained bilinear control problem defined on a probability simplex is solved approximately using a receding horizon control scheme. To achieve robust tasking of wireless airborne networks, we have set up a multiple objective cross-layer optimization framework to compare various multi-hop clustered cooperative transmission schemes. Within this framework, parameters such as cluster size, transmission power and hop patterns can be optimized to enhance signal-to-noise plus interference ratio (SINR), transmission throughput and antijamming capability, under the constraint on ISR coverage and network reliability. Our analysis and simulation results indicate that the optimization is in favor of smaller cluster size and shorter transmission distance.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2009
Accession Number
ADA495913

Entities

People

  • Matthew Ruschmann
  • N. E. Wu
  • Xiaohua E. Li

Organizations

  • State University of New York

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Air Force Research Laboratories
  • Airborne
  • Algorithms
  • Coding
  • Computer Programming
  • Control Systems
  • Data Rate
  • Markov Chains
  • Markov Processes
  • Optimization
  • Probability
  • Random Variables
  • Reliability
  • Simulations
  • Stochastic Processes
  • Transitions
  • Uncertainty

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Computer Networking
  • Radio communications and signal processing.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - DoD AI Strategy
  • AI & ML - Machine Learning Algorithms
  • Space