IEICE TRANSACTIONS on Communications
A Lightweight Automatic Modulation Recognition Algorithm Based on Deep Learning
Summary :
Automatic modulation recognition (AMR) plays a critical role in modern communication systems. Owing to the recent advancements of deep learning (DL) techniques, the application of DL has been widely studied in AMR, and a large number of DL-AMR algorithms with high recognition rates have been developed. Most DL-AMR algorithm models have high recognition accuracy but have numerous parameters and are huge, complex models, which make them hard to deploy on resource-constrained platforms, such as satellite platforms. Some lightweight and low-complexity DL-AMR algorithm models also struggle to meet the accuracy requirements. Based on this, this paper proposes a lightweight and high-recognition-rate DL-AMR algorithm model called Lightweight Densely Connected Convolutional Network (DenseNet) Long Short-Term Memory network (LDLSTM). The model cascade of DenseNet and LSTM can achieve the same recognition accuracy as other advanced DL-AMR algorithms, but the parameter volume is only 1/12 that of these algorithms. Thus, it is advantageous to deploy LDLSTM in resource-constrained systems.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E106-B No.4 pp.367-373
- Publication Date
- 2023/04/01
- Publicized
- 2022/09/30
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.2022EBP3087
- Type of Manuscript
- PAPER
- Category
- Wireless Communication Technologies
Authors
Dong YI
PLA Strategic Support Force Information Engineering University
Di WU
PLA Strategic Support Force Information Engineering University
Tao HU
PLA Strategic Support Force Information Engineering University
Keyword
Latest Issue
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Dong YI, Di WU, Tao HU, "A Lightweight Automatic Modulation Recognition Algorithm Based on Deep Learning" in IEICE TRANSACTIONS on Communications,
vol. E106-B, no. 4, pp. 367-373, April 2023, doi: 10.1587/transcom.2022EBP3087.
Abstract: Automatic modulation recognition (AMR) plays a critical role in modern communication systems. Owing to the recent advancements of deep learning (DL) techniques, the application of DL has been widely studied in AMR, and a large number of DL-AMR algorithms with high recognition rates have been developed. Most DL-AMR algorithm models have high recognition accuracy but have numerous parameters and are huge, complex models, which make them hard to deploy on resource-constrained platforms, such as satellite platforms. Some lightweight and low-complexity DL-AMR algorithm models also struggle to meet the accuracy requirements. Based on this, this paper proposes a lightweight and high-recognition-rate DL-AMR algorithm model called Lightweight Densely Connected Convolutional Network (DenseNet) Long Short-Term Memory network (LDLSTM). The model cascade of DenseNet and LSTM can achieve the same recognition accuracy as other advanced DL-AMR algorithms, but the parameter volume is only 1/12 that of these algorithms. Thus, it is advantageous to deploy LDLSTM in resource-constrained systems.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2022EBP3087/_p
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@ARTICLE{e106-b_4_367,
author={Dong YI, Di WU, Tao HU, },
journal={IEICE TRANSACTIONS on Communications},
title={A Lightweight Automatic Modulation Recognition Algorithm Based on Deep Learning},
year={2023},
volume={E106-B},
number={4},
pages={367-373},
abstract={Automatic modulation recognition (AMR) plays a critical role in modern communication systems. Owing to the recent advancements of deep learning (DL) techniques, the application of DL has been widely studied in AMR, and a large number of DL-AMR algorithms with high recognition rates have been developed. Most DL-AMR algorithm models have high recognition accuracy but have numerous parameters and are huge, complex models, which make them hard to deploy on resource-constrained platforms, such as satellite platforms. Some lightweight and low-complexity DL-AMR algorithm models also struggle to meet the accuracy requirements. Based on this, this paper proposes a lightweight and high-recognition-rate DL-AMR algorithm model called Lightweight Densely Connected Convolutional Network (DenseNet) Long Short-Term Memory network (LDLSTM). The model cascade of DenseNet and LSTM can achieve the same recognition accuracy as other advanced DL-AMR algorithms, but the parameter volume is only 1/12 that of these algorithms. Thus, it is advantageous to deploy LDLSTM in resource-constrained systems.},
keywords={},
doi={10.1587/transcom.2022EBP3087},
ISSN={1745-1345},
month={April},}
Copy
TY - JOUR
TI - A Lightweight Automatic Modulation Recognition Algorithm Based on Deep Learning
T2 - IEICE TRANSACTIONS on Communications
SP - 367
EP - 373
AU - Dong YI
AU - Di WU
AU - Tao HU
PY - 2023
DO - 10.1587/transcom.2022EBP3087
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E106-B
IS - 4
JA - IEICE TRANSACTIONS on Communications
Y1 - April 2023
AB - Automatic modulation recognition (AMR) plays a critical role in modern communication systems. Owing to the recent advancements of deep learning (DL) techniques, the application of DL has been widely studied in AMR, and a large number of DL-AMR algorithms with high recognition rates have been developed. Most DL-AMR algorithm models have high recognition accuracy but have numerous parameters and are huge, complex models, which make them hard to deploy on resource-constrained platforms, such as satellite platforms. Some lightweight and low-complexity DL-AMR algorithm models also struggle to meet the accuracy requirements. Based on this, this paper proposes a lightweight and high-recognition-rate DL-AMR algorithm model called Lightweight Densely Connected Convolutional Network (DenseNet) Long Short-Term Memory network (LDLSTM). The model cascade of DenseNet and LSTM can achieve the same recognition accuracy as other advanced DL-AMR algorithms, but the parameter volume is only 1/12 that of these algorithms. Thus, it is advantageous to deploy LDLSTM in resource-constrained systems.
ER -
English
