IEEE Access (Jan 2018)

Airborne Cognitive Networking: Design, Development, and Deployment

  • George Sklivanitis,
  • Adam Gannon,
  • Konstantinos Tountas,
  • Dimitris A. Pados,
  • Stella N. Batalama,
  • Stephen Reichhart,
  • Michael Medley,
  • Ngwe Thawdar,
  • Ulysses Lee,
  • John D. Matyjas,
  • Scott Pudlewski,
  • Andrew Drozd,
  • Ashwin Amanna,
  • Fred Latus,
  • Zachary Goldsmith,
  • David Diaz

DOI
https://doi.org/10.1109/ACCESS.2018.2857843
Journal volume & issue
Vol. 6
pp. 47217 – 47239

Abstract

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We design, develop, and experimentally validate a complete integrated software/hardware platform for airborne cognitive networking in both indoor and outdoor environments. We first present the concept of all-spectrum cognitive networking and describe a distributed algorithm for maximizing network spectral efficiency by jointly optimizing channel access code-waveforms and routes in a multi-hop network. We then discuss system design parameters and implementation details for setting up a software-defined radio (SDR) testbed that enables reconfigurability at the physical (PHY), medium-access control (MAC), and network (NET) layers of the network protocol stack, either by a user or by means of autonomous decisions. Our algorithmic developments toward spectrally-efficient cognitive networking are software optimized on heterogeneous multi-core general-purpose processor-based SDR architectures by leveraging the design of a novel software-radio framework that offers self-optimization and real-time adaptation capabilities at the PHY, MAC, and NET layers of the network protocol stack. We verify our system design approach in a large-scale testbed deployment of ten terrestrial and one airborne SDR platforms at the Stockbridge Controllable Contested Environment at the Air Force Research Laboratory, Rome, NY, USA. Proof-ofconcept experimental results from both indoor and outdoor testbed deployments show that the proposed system can be used to build all-spectrum cognitive networks that withstand intentional interference at PHY and NET layers and can cognitively coexist with non-cross-layer optimized networks.

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