TY - JOUR
T1 - Implementation and On-Orbit Testing Results of a Space Communications Cognitive Engine
AU - Hackett, Timothy M.
AU - Bilen, Sven G.
AU - Ferreira, Paulo Victor Rodrigues
AU - Wyglinski, Alexander M.
AU - Reinhart, Richard C.
AU - Mortensen, Dale J.
N1 - Funding Information:
Manuscript received February 28, 2018; revised August 25, 2018; accepted October 21, 2018. Date of publication October 26, 2018; date of current version December 21, 2018. 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 associate editor coordinating the review of this paper and approving it for publication was A. B. MacKenzie. (Corresponding author: Timothy M. Hackett.) T. M. Hackett and S. G. Bilén are with the School of Electrical Engineering and Computer Science, Pennsylvania State University, University Park, PA 16802 USA (e-mail: tmh5344@psu.edu; sbilen@psu.edu).
Publisher Copyright:
© 2015 IEEE.
PY - 2018/12
Y1 - 2018/12
N2 - Cognitive algorithms for communications systems have been presented in literature, but very few have been integrated into a fielded system, especially space communications systems. In this paper, we describe the implementation of a multi-objective reinforcement-learning algorithm using deep artificial neural networks acting as a radio-resource-allocation controller. The developed software core is generic in nature and can be ported readily to another application. The cognitive engine algorithm implementation was characterized through a series of tests using both a ground-based system and a space-based system. The ground system comprised of engineering-model software-defined radios, commercial modems, and RF equipment emulating the targeted space-to-ground channel. The on-orbit communication system, including a space-based, remotely controlled transmitter, resides on the International Space Station and operates with a ground-based receiver at NASA Glenn Research Center. Through a series of on-orbit tests, the cognitive engine was tested in a highly dynamic channel and its performance is discussed and analyzed.
AB - Cognitive algorithms for communications systems have been presented in literature, but very few have been integrated into a fielded system, especially space communications systems. In this paper, we describe the implementation of a multi-objective reinforcement-learning algorithm using deep artificial neural networks acting as a radio-resource-allocation controller. The developed software core is generic in nature and can be ported readily to another application. The cognitive engine algorithm implementation was characterized through a series of tests using both a ground-based system and a space-based system. The ground system comprised of engineering-model software-defined radios, commercial modems, and RF equipment emulating the targeted space-to-ground channel. The on-orbit communication system, including a space-based, remotely controlled transmitter, resides on the International Space Station and operates with a ground-based receiver at NASA Glenn Research Center. Through a series of on-orbit tests, the cognitive engine was tested in a highly dynamic channel and its performance is discussed and analyzed.
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U2 - 10.1109/TCCN.2018.2878202
DO - 10.1109/TCCN.2018.2878202
M3 - Article
AN - SCOPUS:85059200867
VL - 4
SP - 825
EP - 842
JO - IEEE Transactions on Cognitive Communications and Networking
JF - IEEE Transactions on Cognitive Communications and Networking
SN - 2332-7731
IS - 4
M1 - 8510837
ER -