Cooperative Self-Learning: A Framework for Few-Shot Jamming Identification

SHI Yuxin; LU Xinjin; SUN Yifu; AN Kang; LI Yusheng

doi:10.23919/cje.2023.00.229

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Yuxin SHI, Xinjin LU, Yifu SUN, et al., “Cooperative Self-Learning: A Framework for Few-Shot Jamming Identification,” Chinese Journal of Electronics, vol. 33, no. 4, pp. 1–8, 2024 doi: 10.23919/cje.2023.00.229

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Yuxin SHI, Xinjin LU, Yifu SUN, et al., “Cooperative Self-Learning: A Framework for Few-Shot Jamming Identification,” Chinese Journal of Electronics, vol. 33, no. 4, pp. 1–8, 2024 doi: 10.23919/cje.2023.00.229

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Cooperative Self-Learning: A Framework for Few-Shot Jamming Identification

doi: 10.23919/cje.2023.00.229

SHI Yuxin^{1, 2
,},
LU Xinjin^2
,,
SUN Yifu^1
,,
AN Kang^1
,,
LI Yusheng^{1
,
,}

1.
The Sixty-Third Research Institute, National University of Defense Technology, Nanjing 210000, China
2.
The College of Electronic Science, National University of Defense Technology, Changsha 410000, China

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Author Bio:
Yuxin SHI received the B.S. degree in Communication Engineering M.E. degree in Information and Communication Engineering from the National University of Defense Technology (NUDT), Changsha, Hunan, China, in 2016 and 2019, respectively. He is currently pursuing the Ph.D. degree with the Sixty-third Research Institute at NUDT, Nanjing, China. His research interests include index modulation, anti-jamming communication and physical layer security. (Email: shiyuxin13@nudt.edu.cn)

Xinjin LU received the B.S degree in Communication Engineering from the Hunan University (HNU) in 2016. She received the M.S. degree in Information and Communication Engineering from National University of Defence technology (NUDT) in 2019. She is currently pursuing the Ph.D degree in the College of Electronic Science and Technology from NUDT. Her research interests include channel coding, physical layer security, channel coding and modulation. (Email: luxinjin17@nudt.edu.cn)

Yifu SUN received the B.E. in Communications Engineering from National University of DefenseTechnology (NUDT), Changsha, China, in 2019, where he is currently pursuing the Ph.D. degree in Information and Communications Engineering with College of Electronic Science and Technology. His current research interests are in anti-jamming communications, reconfigurable intelligent surface, physical layer security, cooperative and cognitive communications, massive MIMO systems, and signal processing for wireless communications. (Email: sunyifu.nudt@nudt.edu.cn)

Kang AN received the B.E. degree in Electronic Engineering from Nanjing University of Aeronautics and Astronautics, Nanjing, China, in 2011, and PhD degree in Communication Engineering from Army Engineering University, Nanjing, China, in 2017. He is currently an associate professor with the Sixty-third Research Institute, National University of Defense Technology, Nanjing, China. His current research interests include reconfigurable intelligent surface, anti-jamming communications, satellite/aerial communications, physical-layer security, signal processing and machine learning for wireless communications. He has published more than 100 peer-reviewed research papers in leading journals and flagship conferences and many of them are ESI highly cited papers. He was listed in the World’s Top 2% Scientists identified by Stanford University in 2022. He was a recipient of exemplary Reviewer for IEEE Transactions on Communications and IEEE Communications Letters in 2022. He was the recipient of the Outstanding Ph.D. Thesis Award of Chinese Institute of Command and Control in 2019. He is also serving as an Editor for Frontiers in Communications and Networks and Frontiers in Space Technologies. He was the corecipient of the 2023 IEEE IWCMC Best Paper Awards. (Email: ankang89@nudt.edu.cn)

Yusheng LI received the M.S. degree from the Nanjing Institute of Communications Engineering, Nanjing, China, in 2000. He is currently a Professor with the Sixty-third Research Institute, National University of Defense Technology, and also with the Nanjing Telecommunication Technology Institute, Nanjing, China. His research interests include digital signal processing in anti-jamming communications. (Email: lys63s@163.com)
Corresponding author: Email: lys63s@163.com
Received Date: 2023-03-22
Accepted Date: 2023-12-12

Available Online: 2022-03-22

Abstract

Abstract

Jamming identification is the key objective behind effective anti-jamming methods. Due to the requirement of low-complexity and the condition of few labeled shots for a real jamming identification, it is very challenging to identify jamming patterns with high accuracy. To this end, we first propose a general framework of cooperative jamming identification among multiple nodes. Moreover, we further propose a novel fusion center (FC) aided self-learning scheme, which uses the guidance of the FC to improve the effectiveness of the identification. Simulations show that the proposed framework of the cooperative jamming identification can significantly enhance the average accuracy with low-complexity. It is also demonstrated that the proposed FC aided self-learning scheme has the superior average accuracy compared with other identification schemes, which is very effective especially in the few labeled jamming shots scenarios.
- Jamming identification,
- Multiple nodes,
- Self-learning,
- Few labeled shots

1In this paper, the labeled shots denote the received jamming signals with the known jamming patterns by the manual labeling, whilst the unlabeled shots denote the received jamming signals with unknown jamming patterns.
2Here, since different features can represent the characteristics of the jamming signal, we use the four previously mentioned jamming features to extract the information of the jamming signal, which have been used in jamming identification [9]. Also, using features can decrease the dimension of the input of classifier from $ l $ to 4, which helps to remove the high burden of the computational complexity. Although there may exist other features with better performance, the selection of the features is beyond the scope of this paper.
3Though the node of the FC aided self-learning scheme requires to receive the labels from the FC, it does not introduce the noticeable complexity compared to the node based self-learning scheme.

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References(23)

References

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