Theories and Algorithms to Achieve Linear Capacity Scaling in Wireless Networks through Opportunistic Usage of Direct Energy Links

Abstract

The objectives of the project are to: 1) investigate the fundamental capacity limits of wireless networks with DE links, and the conditions under which close-to-linear capacity scaling is achievable; 2) investigate the practical network algorithms and protocols that can achieve closeto- linear capacity scaling. Previous research on DE links mostly focus on acquiring and maintaining reliable DE links. In this project, we take a different approach and ask how best to utilize DE links even when they are unreliable and unpredictable. We hypothesize that we can obtain a significant capacity increase in a large-scale network by averaging over many random unreliable DE links. Our approach is motivated by the recent advances made in the network science, namely, the small-world phenomenon, in which the network exhibits excellent performance when it is connected by a mixture of structured short-distance and random longdistance links. Using a mixture of DE and OD links, we hope to obtain similar performance gain.

Document Details

Document Type
DoD Grant Award
Publication Date
Feb 12, 2016
Source ID
W911NF1510393

Entities

People

  • Hong Huang

Organizations

  • Army Contracting Command
  • New Mexico State University
  • Office of the Secretary of Defense

Tags

Fields of Study

  • Computer science

Readers

  • Computer Networking
  • Neural Network Machine Learning.
  • Wave Propagation and Nonlinear Chaotic Dynamics.

Technology Areas

  • Directed Energy