TY - GEN
T1 - Implementation of a space communications cognitive engine
AU - Hackett, Timothy M.
AU - Bilén, Sven G.
AU - Ferreira, Paulo Victor R.
AU - Wyglinski, Alexander M.
AU - Reinhart, Richard C.
N1 - Funding Information:
ACKNOWLEDGMENT This work was supported by a NASA Space Technology Research Fellowship (grant number NNX15AQ41H) and a cooperative agreement with NASA John H. Glenn Research Center (grant number NNC14AA01A). The authors would also like to thank Joseph Downey, Michael Evans, Dale Mortensen, and the rest of the Cognitive Communications Project team at NASA GRC for all of their help and support.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/8/3
Y1 - 2017/8/3
N2 - Although communications-based cognitive engines have been proposed, very few have been implemented in a full system, especially in a space communications system. In this paper, we detail the implementation of a multi-objective reinforcement-learning algorithm and deep artificial neural networks for the use as a radio-resource-allocation controller. The modular software architecture presented encourages re-use and easy modification for trying different algorithms. Various trade studies involved with the system implementation and integration are discussed. These include the choice of software libraries that provide platform flexibility and promote reusability, choices regarding the deployment of this cognitive engine within a system architecture using the DVB-S2 standard and commercial hardware, and constraints placed on the cognitive engine caused by real-world radio constraints. The implemented radio-resource-allocation-management controller was then integrated with the larger space-ground system developed by NASA Glenn Research Center (GRC).
AB - Although communications-based cognitive engines have been proposed, very few have been implemented in a full system, especially in a space communications system. In this paper, we detail the implementation of a multi-objective reinforcement-learning algorithm and deep artificial neural networks for the use as a radio-resource-allocation controller. The modular software architecture presented encourages re-use and easy modification for trying different algorithms. Various trade studies involved with the system implementation and integration are discussed. These include the choice of software libraries that provide platform flexibility and promote reusability, choices regarding the deployment of this cognitive engine within a system architecture using the DVB-S2 standard and commercial hardware, and constraints placed on the cognitive engine caused by real-world radio constraints. The implemented radio-resource-allocation-management controller was then integrated with the larger space-ground system developed by NASA Glenn Research Center (GRC).
UR - http://www.scopus.com/inward/record.url?scp=85030249695&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85030249695&partnerID=8YFLogxK
U2 - 10.1109/CCAAW.2017.8001607
DO - 10.1109/CCAAW.2017.8001607
M3 - Conference contribution
AN - SCOPUS:85030249695
T3 - 2017 Cognitive Communications for Aerospace Applications Workshop, CCAA 2017
BT - 2017 Cognitive Communications for Aerospace Applications Workshop, CCAA 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 Cognitive Communications for Aerospace Applications Workshop, CCAA 2017
Y2 - 27 June 2017 through 28 June 2017
ER -