TY - GEN
T1 - Deepmod
T2 - 2018 IEEE Military Communications Conference, MILCOM 2018
AU - Anderson, Adam
AU - Young, Steven R.
AU - Karnowski, Thomas P.
AU - Vann, Jason M.
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - 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.
AB - 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.
KW - Digital communications
KW - Machine learning
KW - Physical layer
KW - Tactical networks
UR - http://www.scopus.com/inward/record.url?scp=85061448415&partnerID=8YFLogxK
U2 - 10.1109/MILCOM.2018.8599807
DO - 10.1109/MILCOM.2018.8599807
M3 - Conference contribution
AN - SCOPUS:85061448415
T3 - Proceedings - IEEE Military Communications Conference MILCOM
SP - 213
EP - 218
BT - 2018 IEEE Military Communications Conference, MILCOM 2018
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 29 October 2018 through 31 October 2018
ER -