A CNN-Based Adaptive Federated Learning Approach for Communication Jamming Recognition
Abstract
:1. Introduction
- Diverse Signal Characteristics: Jamming signals can exhibit diverse characteristics depending on the jammer’s technology and strategy. Identifying these varied signals consistently is challenging.
- Data Limitations: Accurate recognition often requires a substantial amount of data for training machine learning models. However, in real-world scenarios, obtaining a vast and varied dataset is a challenge.
- Distributed Data: The data is decentralized and stored in local nodes, and cannot be directly uploaded for network training due to the sensitivity of communication jamming data.
1.1. Related Works
1.2. Novelty and Main Contributions
- Firstly, we introduce a deep CNN-based federated learning framework for jamming recognition. This allows each local node model not only to acquire valuable information from the central node but to also contribute its gradient information.
- Secondly, we conceptualize an adaptive adjustment mechanism for the mixed weight parameter of both the local and global models. This can autonomously strike a balance between the global model and the collective knowledge of local data across devices.
- Thirdly, we demonstrate the robustness of parameter and the superiority of our algorithm through experiments.
2. System Model
3. Proposed Algorithm
- Non-IID: The jamming signals collected by each cognitive node are different due to the different positions of cognitive nodes. As a result, any particular node’s local dataset may not be representative of the global distribution.
- Unbalanced: Some cognitive nodes have jamming signals and some do not, leading to varying amounts of local training data.
- Limited communication: Cognitive nodes are on slow or expensive connections.
3.1. CNN Network Structure
3.2. Adaptive Federated Learning
Algorithm 1: Adaptive Federated Learning. |
|
|
3.3. Parameter Update
4. Simulation
4.1. Experiment Setup
4.2. Experiment Result
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Yao, F. Communication Anti-Interference Engineering and Practice; Electronic Industry Press: Beijing, China, 2012. [Google Scholar]
- Gao, Y.; Fan, H.; Ren, L.; Liu, Z.; Liu, Q.; Mao, E. Joint Design of Waveform and Mismatched Filter for Interrupted Sampling Repeater Jamming Suppression. IEEE Trans. Aerosp. Electron. Syst. 2023. [Google Scholar] [CrossRef]
- Almasoud, A.M. Robust Anti-Jamming Technique for UAV Data Collection in IoT Using Landing Platforms and RIS. IEEE Access 2023, 11, 70635–70651. [Google Scholar] [CrossRef]
- Yang, T.; Yuan, Y.; Yi, W. Multi-Domain Resource Scheduling for Surveillance Radar Anti-Jamming based on Q-Learning. In Proceedings of the 2023 IEEE Radar Conference (RadarConf23), San Antonio, TX, USA, 1–5 May 2023. [Google Scholar] [CrossRef]
- Zhou, Q.; Niu, Y.; Xiang, P.; Li, Y. Intra-Domain Knowledge Reuse Assisted Reinforcement Learning for Fast Anti-Jamming Communication. IEEE Trans. Inf. Forensics Secur. 2023. [Google Scholar] [CrossRef]
- Han, C.; Huo, L.; Tong, X.; Wang, H.; Liu, X. Spatial anti-jamming scheme for internet of satellites based on the deep reinforcement learning and stackelberg game. IEEE Trans. Veh. Technol. 2020, 69, 5331–5342. [Google Scholar] [CrossRef]
- Aboueleneen, N.; Alwarafy, A.; Abdallah, M. Secure and Energy-Efficient Communication for Internet of Drones Networks: A Deep Reinforcement Learning Approach. In Proceedings of the 2023 International Wireless Communications and Mobile Computing (IWCMC), Marrakesh, Morocco, 19–23 June 2023; pp. 818–823. [Google Scholar]
- Mughal, M.O.; Kim, S. Signal classification and jamming detection in wide-band radios using naïve bayes classifier. IEEE Commun. 2018, 22, 1398–1401. [Google Scholar] [CrossRef]
- Yi, W.; Qu, Y.; Li, S.; Liu, Q. Hierarchical Jamming Recognition with Spectrum Fusion Feature and Twin-bound SVM for Cognitive Satellite Communications. In Proceedings of the 2023 IEEE Wireless Communications and Networking Conference (WCNC), Glasgow, UK, 26–29 March 2023. [Google Scholar] [CrossRef]
- Zhou, H.; Wang, Z.; Wu, R.; Xiong, X. Jamming Recognition Algorithm Based on Variational Mode Decomposition. IEEE Sens. J. 2023, 23, 17341–17349. [Google Scholar] [CrossRef]
- Wang, G.; Wang, Y.; Huang, G. Classification Methods with Signal Approximation for Unknown Interference. IEEE Access 2020, 8, 37933–37945. [Google Scholar] [CrossRef]
- Kong, L.; Xu, Z.; Wang, J. A novel algorithm for jamming recognition in wireless communication. In Proceedings of the 2013 6th International Congress on Image and Signal Processing (CISP), Hangzhou, China, 16–18 December 2013; Volume 3, pp. 1473–1477. [Google Scholar]
- Niu, Y.; Cheng, Y.; Chen, J. Jamming pattern recognition based on complexity measure. In Proceedings of the 2010 3rd International Congress on Image and Signal Processing, Yantai, China, 16–18 October 2010; Volume 8, pp. 3596–3600. [Google Scholar]
- Shi, Y.Y.; Lu, X.; Niu, Y.; Li, Y. Efficient jamming identification in wireless communication: Using small sample data driven naive bayes classifier. IEEE Wirel. Commun. Lett. 2021, 10, 1375–1379. [Google Scholar] [CrossRef]
- Roopak, M.; Tian, G.Y.; Chambers, J. Deep Learning Models for Cyber Security in IoT Networks. In Proceedings of the 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA, 7–9 January 2019; pp. 0452–0457. [Google Scholar]
- Zhang, H.; Yu, F.; Yan, L.; Wang, T. Key Technologies of Communication Security Detection between Heterogeneous Systems Based on Communication Gateway. In Proceedings of the 2023 IEEE 3rd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA), Chongqing, China, 26–28 May 2023; pp. 258–262. [Google Scholar]
- Yan, B.; Zheng, J.; Li, R.; Fu, K.; Chen, P.; Jia, G.; Shi, Y.; Lv, J.; Gao, B. Semi-supervised pipeline anomaly detection algorithm based on memory items and metric learning. Nondestruct. Test. Eval. 2023. [Google Scholar] [CrossRef]
- Yang, Z.; Huo, L. Bolt preload monitoring based on percussion sound signal and convolutional neural network (CNN). Nondestruct. Test. Eval. 2022, 37, 464–481. [Google Scholar] [CrossRef]
- Toma, A.; Cecchinato, N.; Drioli, C.; Foresti, G.L.; Ferrin, G. CNN-based processing of radio frequency signals for augmenting acoustic source localization and enhancement in UAV security applications. In Proceedings of the 2021 International Conference on Military Communication and Information Systems (ICMCIS), The Hague, The Netherlands, 4–5 May 2021. [Google Scholar] [CrossRef]
- O’Shea, T.J.; Roy, T.; Clancy, T.C. Over-the-air deep learning based radio signal classification. IEEE J. Sel. Top. Signal Process. 2018, 12, 168–179. [Google Scholar] [CrossRef] [Green Version]
- Shao, G.; Chen, Y.; Wei, Y. Convolutional neural network-based radar jamming signal classification with sufficient and limited samples. IEEE Access 2020, 8, 588–598. [Google Scholar] [CrossRef]
- Hou, L.; Zhang, S.; Wang, C.; Li, X.; Chen, S.; Zhu, L.; Zhu, Y. Jamming Recognition of Carrier-Free UWB Cognitive Radar Based on MANet. IEEE Trans. Instrum. Meas. 2023, 72, 8504413. [Google Scholar] [CrossRef]
- Zhou, H.; Wang, L.; Guo, Z. Recognition of Radar Compound Jamming Based on Convolutional Neural Network. IEEE Trans. Aerosp. Electron. Syst. 2023. [Google Scholar] [CrossRef]
- Shen, J.; Li, Y. Cooperative multi-node cognition method based on deep residual network. Electronics 2022, 11, 3280. [Google Scholar] [CrossRef]
- Wang, S.; Tuor, T.; Salonidis, T.; Leung, K.K.; Makaya, C.; He, T.; Chan, K. Efficient Adaptive federated learning in resource constrained edge computing systems. IEEE J. Sel. Areas Commun. 2021, 37, 1205–1221. [Google Scholar] [CrossRef] [Green Version]
- Lim, W.Y.B.; Luong, N.C.; Hoang, D.T.; Jiao, Y.; Liang, Y.-C.; Yang, Q.; Niyato, D.; Miao, C. Federated learning in mobile edge networks: A comprehensive survey. IEEE Commun. Surv. Tutor. 2020, 22, 2031–2063. [Google Scholar] [CrossRef] [Green Version]
- Zhang, L.; Wu, Y.; Chen, L.; Fan, L.; Nallanathan, A. Scoring Aided Federated Learning on Long-tailed Data for Wireless IoMT based Healthcare System. IEEE J. Biomed. Health Inform. 2023. [Google Scholar] [CrossRef] [PubMed]
- Roy, S.; Li, J.; Bai, Y. Federated Learning-Based Intrusion Detection System for IoT Environments with Locally Adapted Model. In Proceedings of the 2023 IEEE 10th International Conference on Cyber Security and Cloud Computing (CSCloud)/2023 IEEE 9th International Conference on Edge Computing and Scalable Cloud (EdgeCom), Xiangtan, China, 1–3 July 2023; pp. 203–209. [Google Scholar]
- Liu, M.; Liu, Z.; Lu, W.; Chen, Y.; Gao, X.; Zhao, N. Distributed few-shot learning for intelligent recognition of communication jamming. IEEE J. Sel. Top. Signal Process. 2022, 16, 395–405. [Google Scholar] [CrossRef]
- Li, Q.; He, B.; Song, D. Model-Contrastive Federated Learning. In Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA, 20–25 June 2021; pp. 10708–10717. [Google Scholar]
- Lin, J.; Wu, X. Personalized Federated Learning with Data Heterogeneity Constraints. In Proceedings of the 2022 3rd International Conference on Computer Science and Management Technology (ICCSMT), Shanghai, China, 18–20 November 2022; pp. 152–155. [Google Scholar]
Index | Layers | Output Dimension |
---|---|---|
1 | Input | (128, 2, 1) |
2 | Convolution Module (16) | (64, 2, 16) |
3 | Convolution Module (32) | (32, 2, 32) |
4 | Convolution Module (32) | (16, 2, 32) |
5 | Convolution Module (64) | (8, 2, 64) |
6 | Max Pooling Layer | (4, 2, 64) |
7 | Full Connected Layer | (8, 1) |
Algorithm | Total Time | Memory Used |
---|---|---|
FedMeta | 12.40 s | 28.70 MB |
FedPer | 11.89 s | 28.65 MB |
Moon | 24.97 s | 43.05 MB |
AFL | 17.91 s | 35.12 MB |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Zhang, N.; Li, Y.; Shi, Y.; Shen, J. A CNN-Based Adaptive Federated Learning Approach for Communication Jamming Recognition. Electronics 2023, 12, 3425. https://doi.org/10.3390/electronics12163425
Zhang N, Li Y, Shi Y, Shen J. A CNN-Based Adaptive Federated Learning Approach for Communication Jamming Recognition. Electronics. 2023; 12(16):3425. https://doi.org/10.3390/electronics12163425
Chicago/Turabian StyleZhang, Ningsong, Yusheng Li, Yuxin Shi, and Junren Shen. 2023. "A CNN-Based Adaptive Federated Learning Approach for Communication Jamming Recognition" Electronics 12, no. 16: 3425. https://doi.org/10.3390/electronics12163425