Abstract
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.
Original language | English (US) |
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Title of host publication | 2018 IEEE International Conference on Communications, ICC 2018 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Volume | 2018-May |
ISBN (Print) | 9781538631805 |
DOIs | |
State | Published - Jul 27 2018 |
Event | 2018 IEEE International Conference on Communications, ICC 2018 - Kansas City, United States Duration: May 20 2018 → May 24 2018 |
Other
Other | 2018 IEEE International Conference on Communications, ICC 2018 |
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Country | United States |
City | Kansas City |
Period | 5/20/18 → 5/24/18 |
ASJC Scopus subject areas
- Computer Networks and Communications
- Electrical and Electronic Engineering