TY - GEN
T1 - Artificial neural network based automatic modulation classification over a software defined radio testbed
AU - Jagannath, Jithin
AU - Polosky, Nicholas
AU - O'Connor, Daniel
AU - Theagarajan, Lakshmi N.
AU - Sheaffer, Brendan
AU - Foulke, Svetlana
AU - Varshney, Pramod K.
N1 - Funding Information:
1ACKNOWLEDGMENT OF SUPPORT AND DISCLAIMER:(a) Contractor acknowledges Government’s support in the publication of this paper. This material is based upon work supported by the US Air Force Research Laboratory under AFRL Contract No. FA8750-16-C-0085. (b) Any opinions, findings and conclusions or recommendation expressed in this material are those of the author(s) and do not necessarily reflect the views of AFRL.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/27
Y1 - 2018/7/27
N2 - In this paper, we design and evaluate a practical AMC system that can be readily deployed to provide robust performance in various real-time commercial scenarios. Thus, our main goal is to develop a robust AMC algorithm with low computational complexity for easy implementation and practical deployment. To this end, we utilize recently revitalized machine learning based approaches used for various classification purposes. In our proposed AMC architecture, we first propose various statistics that serve as features of the AMC signals; next, we design an artificial neural network (ANN) based classifier that performs AMC over a wide range of SNRs. We employ Nesterov accelerated adaptive moment (NADAM) estimation technique to improve the classification performance of our ANN. Further, to establish the practical feasibility of our proposed architecture, we implement it on a SDR testbed. The proposed ANN-based classifier is shown to outperforms the hybrid hierarchical AMC (HH-AMC) system and is flexible enough to easily expand the dictionary of modulation formats for other applications.
AB - In this paper, we design and evaluate a practical AMC system that can be readily deployed to provide robust performance in various real-time commercial scenarios. Thus, our main goal is to develop a robust AMC algorithm with low computational complexity for easy implementation and practical deployment. To this end, we utilize recently revitalized machine learning based approaches used for various classification purposes. In our proposed AMC architecture, we first propose various statistics that serve as features of the AMC signals; next, we design an artificial neural network (ANN) based classifier that performs AMC over a wide range of SNRs. We employ Nesterov accelerated adaptive moment (NADAM) estimation technique to improve the classification performance of our ANN. Further, to establish the practical feasibility of our proposed architecture, we implement it on a SDR testbed. The proposed ANN-based classifier is shown to outperforms the hybrid hierarchical AMC (HH-AMC) system and is flexible enough to easily expand the dictionary of modulation formats for other applications.
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U2 - 10.1109/ICC.2018.8422346
DO - 10.1109/ICC.2018.8422346
M3 - Conference contribution
AN - SCOPUS:85050475681
SN - 9781538631805
T3 - IEEE International Conference on Communications
BT - 2018 IEEE International Conference on Communications, ICC 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Communications, ICC 2018
Y2 - 20 May 2018 through 24 May 2018
ER -