TY - GEN
T1 - Design and evaluation of hierarchical hybrid automatic modulation classifier using software defined radios
AU - Jagannath, Jithin
AU - O'Connor, Dan
AU - Polosky, Nicholas
AU - Sheaffer, Brendan
AU - Foulke, Svetlana
AU - Theagarajan, Lakshmi N.
AU - Varshney, Pramod K.
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/3/1
Y1 - 2017/3/1
N2 - Automatic modulation classification (AMC) is a key component of intelligent communication systems used in various military and cognitive radio applications. In AMC, it is desired to increase the number of different modulation formats that can be classified, reduce the computational complexity of classification, and improve the robustness and accuracy of the classifier. Generally, AMC techniques are classified into feature based (FB) and likelihood based (LB) classifiers. In this paper, we propose a novel hierarchical hybrid automatic modulation classifier (HH-AMC) that employs both feature based and likelihood based classifiers to improve performance and reduce complexity. As another major contribution of this paper, we implement and evaluate the performance of HH-AMC over-the-air (OTA) using software defined radios (SDRs) to demonstrate the feasibility of the proposed scheme in practice. Experimental evaluation shows high probability of correct classification (Pcc) for both linear and non-linear modulation formats including BPSK, QPSK, 8-PSK, 16-QAM, 32-QAM, CPFSK, GFSK and GMSK under lab conditions.
AB - Automatic modulation classification (AMC) is a key component of intelligent communication systems used in various military and cognitive radio applications. In AMC, it is desired to increase the number of different modulation formats that can be classified, reduce the computational complexity of classification, and improve the robustness and accuracy of the classifier. Generally, AMC techniques are classified into feature based (FB) and likelihood based (LB) classifiers. In this paper, we propose a novel hierarchical hybrid automatic modulation classifier (HH-AMC) that employs both feature based and likelihood based classifiers to improve performance and reduce complexity. As another major contribution of this paper, we implement and evaluate the performance of HH-AMC over-the-air (OTA) using software defined radios (SDRs) to demonstrate the feasibility of the proposed scheme in practice. Experimental evaluation shows high probability of correct classification (Pcc) for both linear and non-linear modulation formats including BPSK, QPSK, 8-PSK, 16-QAM, 32-QAM, CPFSK, GFSK and GMSK under lab conditions.
UR - http://www.scopus.com/inward/record.url?scp=85016781457&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85016781457&partnerID=8YFLogxK
U2 - 10.1109/CCWC.2017.7868362
DO - 10.1109/CCWC.2017.7868362
M3 - Conference contribution
AN - SCOPUS:85016781457
T3 - 2017 IEEE 7th Annual Computing and Communication Workshop and Conference, CCWC 2017
BT - 2017 IEEE 7th Annual Computing and Communication Workshop and Conference, CCWC 2017
A2 - Saha, Himadri Nath
A2 - Chakrabarti, Satyajit
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2017
Y2 - 9 January 2017 through 11 January 2017
ER -