AF Relaying Secrecy Performance Prediction for 6G Mobile Communication Networks in Industry 5.0
Abstract
Industry 5.0 has developed in full swing, and accelerated the process of the sixth-generation (6G) mobile communication. Physical layer security is important for complex 6G mobile communication networks. To process active complex events in 6G mobile cooperative networks, predicting secrecy performance in time is essential for the mobile communication quality evaluation. Using amplify-and-forward (AF) relaying, we propose a transmit antenna selection (TAS) based secrecy scheme in this article. To analyze the security of 6G mobile cooperative networks, signal-to-noise ratio of the end-to-end link is used to derive the novel expressions for secrecy outage probability (SOP). The theoretical results are confirmed by simulation results. Then, we use SOP as the important merit to evaluate the secrecy performance, and set up the dataset. To achieve secrecy performance prediction, a convolutional neural network (CNN) based SOP prediction algorithm is proposed. The designed CNN model has five convolution layers, which all use the same convolution in the padding and do not change data size. For this improved CNN structure, we adopt the idea of SqueezeNet, which belongs to the lightweight CNN. The improved CNN model can greatly reduce the parameters and network complexity on the premise of ensuring the prediction accuracy. We also examine the following state-of-the-art techniques, first, Elman, second, InceptionNet, third, deep neural network (DNN), and fourth, support vector machine methods. The proposed CNN algorithm can achieve better SOP prediction results than other existing methods. In particular, compared with DNN method, the prediction accuracy is increased by 66.7%.
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In FL-blockchain for Industry 5.0, initial parameters are passed through blockchain blocks for secure transmission.
“The initial parameters then passed through the blocks of the blockchain technology for secure transmission [246, 247].”
From Federated learning-based AI approaches in smart healthcare: Concepts, taxonomies, challenges and open issues (2023) · cites this paper as [246]
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- Authors
- Xu, L., Zhou, X., Tao, Y., Yu, X., Yu, M., & Khan, F.
- Affiliations
- Qingdao University of Science and Technology (CN)
- Qingdao University (CN)
- Abdul Wali Khan University Mardan (PK)
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2022-08
- Volume
- 18
- Issue
- 8
- Pages
- 5485-5493
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W3206286397
- Cited by
- 50
- References
- 30
- Citations by year
- 2026: 2 · 2025: 9 · 2024: 20 · 2023: 13 · 2022: 5 · 2021: 1
- FWCI
- 2.86
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Industrial Informatics (publishedVersion)
- Funders
- Natural Science Foundation of Shandong Province (ZR2020QF003)
- National Natural Science Foundation of China (6217072142)
- Key Laboratory of Computer Network and Information Integration
- Ministry of Education (K93-9-2021-09)
- Anhui Engineering Technology Research Center of Automotive New Technique (QCKJ202101)
- Shandong Province Colleges and Universities Young Talents Initiation Program (2019KJN047)
- Doctoral Found of QUST (1203043003480)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Wireless Communication Security Techniques
- Physical Sciences › Computer Science › Computer Networks and Communications › Cybersecurity and Information Systems
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Wireless Communication Technologies
- Keywords
- amplify-and-forward relaying, physical layer security, secrecy outage probability, transmit antenna selection, 6G, cooperative networks, convolutional neural networks, SqueezeNet, lightweight convolutional neural network, Industry 5.0
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