TY - JOUR
T1 - Transfer Learning for Semi-Supervised Automatic Modulation Classification in ZF-MIMO Systems
AU - Wang, Yu
AU - Gui, Guan
AU - Gacanin, Haris
AU - Ohtsuki, Tomoaki
AU - Sari, Hikmet
AU - Adachi, Fumiyuki
N1 - Funding Information:
This work was supported in part by the Project Funded by the National Science and Technology Major Project of the Ministry of Science and Technology of China under Grant TC190A3WZ-2, in part by the National Natural Science Foundation of China under Grant 61901228 and Grant 61671253, in part by the Jiangsu Specially Appointed Professor under Grant RK002STP16001, in part by the Innovation and Entrepreneurship of Jiangsu High-level Talent under Grant CZ0010617002, in part by the Six Top Talents Program of Jiangsu under Grant XYDXX-010, and in part by the 1311 Talent Plan of Nanjing University of Posts and Telecommunications. This article was recommended by Guest Editor Y.-L. Ueng.
Funding Information:
Manuscript received February 18, 2020; revised March 22, 2020, April 8, 2020, and April 21, 2020; accepted April 30, 2020. Date of publication May 4, 2020; date of current version June 12, 2020. This work was supported in part by the Project Funded by the National Science and Technology Major Project of the Ministry of Science and Technology of China under Grant TC190A3WZ-2, in part by the National Natural Science Foundation of China under Grant 61901228 and Grant 61671253, in part by the Jiangsu Specially Appointed Professor under Grant RK002STP16001, in part by the Innovation and Entrepreneurship of Jiangsu High-level Talent under Grant CZ0010617002, in part by the Six Top Talents Program of Jiangsu under Grant XYDXX-010, and in part by the 1311 Talent Plan of Nanjing University of Posts and Telecommunications. This article was recommended by Guest Editor Y.-L. Ueng. (Corresponding author: Guan Gui.) Yu Wang, Guan Gui, and Hikmet Sari are with the College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China (e-mail: 1018010407@njupt.edu.cn; guiguan@njupt.edu.cn; hikmet@njupt.edu.cn).
Publisher Copyright:
© 2011 IEEE.
PY - 2020/6
Y1 - 2020/6
N2 - Automatic modulation classification (AMC) is an essential technology for the non-cooperative communication systems, and it is widely applied into various communications scenarios. In the recent years, deep learning (DL) has been introduced into AMC due to its outstanding identification performance. However, it is almost impossible to implement previously proposed DL-based AMC algorithms without large number of labeled samples, while there are generally few labeled sample and large unlabel samples in the realistic communication scenarios. In this paper, we propose a transfer learning (TL)-based semi-supervised AMC (TL-AMC) in a zero-forcing aided multiple-input and multiple-output (ZF-MIMO) system. TL-AMC has a novel deep reconstruction and classification network (DRCN) structure that consists of convolutional auto-encoder (CAE) and convolutional neural network (CNN). Unlabeled samples flow from CAE for modulation signal reconstruction, while labeled samples are fed into CNN for AMC. Knowledge is transferred from the encoder layer of CAE to the feature layer of CNN by sharing their weights, in order to avoid the ineffective feature extraction of CNN under the limited labeled samples. Simulation results demonstrated the effectiveness of TL-AMC. In detail, TL-AMC performs better than CNN-based AMC under the limited samples. What's more, when compared with CNN-based AMC trained on massive labeled samples, TL-AMC also achieved the similar classification accuracy at the relative high SNR regime.
AB - Automatic modulation classification (AMC) is an essential technology for the non-cooperative communication systems, and it is widely applied into various communications scenarios. In the recent years, deep learning (DL) has been introduced into AMC due to its outstanding identification performance. However, it is almost impossible to implement previously proposed DL-based AMC algorithms without large number of labeled samples, while there are generally few labeled sample and large unlabel samples in the realistic communication scenarios. In this paper, we propose a transfer learning (TL)-based semi-supervised AMC (TL-AMC) in a zero-forcing aided multiple-input and multiple-output (ZF-MIMO) system. TL-AMC has a novel deep reconstruction and classification network (DRCN) structure that consists of convolutional auto-encoder (CAE) and convolutional neural network (CNN). Unlabeled samples flow from CAE for modulation signal reconstruction, while labeled samples are fed into CNN for AMC. Knowledge is transferred from the encoder layer of CAE to the feature layer of CNN by sharing their weights, in order to avoid the ineffective feature extraction of CNN under the limited labeled samples. Simulation results demonstrated the effectiveness of TL-AMC. In detail, TL-AMC performs better than CNN-based AMC under the limited samples. What's more, when compared with CNN-based AMC trained on massive labeled samples, TL-AMC also achieved the similar classification accuracy at the relative high SNR regime.
KW - Automatic modulation classification
KW - convolutional neural network
KW - deep learning
KW - multiple-input and multiple-output
KW - transfer learning
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U2 - 10.1109/JETCAS.2020.2992128
DO - 10.1109/JETCAS.2020.2992128
M3 - Article
AN - SCOPUS:85087199596
VL - 10
SP - 231
EP - 239
JO - IEEE Journal on Emerging and Selected Topics in Circuits and Systems
JF - IEEE Journal on Emerging and Selected Topics in Circuits and Systems
SN - 2156-3357
IS - 2
M1 - 9085414
ER -