Deepmod: An Over-the-Air Trainable Machine Modem for Resilient PHY Layer Communications

Adam Anderson, Steven R. Young, Thomas P. Karnowski, Jason M. Vann

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations

Abstract

Traditional physical layer protocols (e.g. WiFi, WiMax, etc.) are well established and are often optimal in a wide variety of channel conditions including heterogenous links and in tactical communications. Unfortunately, this same optimality encourages ubiquity in wireless communication technology and enhances the potential for catastrophic cyber or physical attacks due to prolific knowledge of underlying physical layers. Any truly resilient communications protocol must be capable of immediate redeployment to meet quality of service (QoS) demands in a wide variety of possible channel media. This work proposes an approach to communications that is contrary to much traditional approaches in that processing blocks are generated real-time and only relevant to the particular channel medium being used. Rather than creating man-made ubiquitous blocks of signal processing, we examine using processing that is immediately expendable once it has been used. This is achieved through software-defined radios, and deep modulation, where system blocks are replaced with machine learning graphs that can be trained, used, and then discarded as needed. Simulation and experimental hardware show how deep modulation can converge to viable communications links, using the same machine intelligence, in vastly different channels.

Original languageEnglish
Title of host publication2018 IEEE Military Communications Conference, MILCOM 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages213-218
Number of pages6
ISBN (Electronic)9781538671856
DOIs
StatePublished - Jul 2 2018
Event2018 IEEE Military Communications Conference, MILCOM 2018 - Los Angeles, United States
Duration: Oct 29 2018Oct 31 2018

Publication series

NameProceedings - IEEE Military Communications Conference MILCOM
Volume2019-October

Conference

Conference2018 IEEE Military Communications Conference, MILCOM 2018
Country/TerritoryUnited States
CityLos Angeles
Period10/29/1810/31/18

Keywords

  • Digital communications
  • Machine learning
  • Physical layer
  • Tactical networks

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