Deep learning–based resource allocation for secure transmission in a non-orthogonal multiple access network
Machine learning techniques, especially deep learning algorithms have been widely utilized to deal with different kinds of research problems in wireless communications. In this article, we investigate the secrecy rate maximization problem in a non-orthogonal multiple access network based on deep lea...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-06-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/15501329221104330 |
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| _version_ | 1832547829354070016 |
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| author | Miao Zhang Yao Zhang Qian Cen Shixun Wu |
| author_facet | Miao Zhang Yao Zhang Qian Cen Shixun Wu |
| author_sort | Miao Zhang |
| collection | DOAJ |
| description | Machine learning techniques, especially deep learning algorithms have been widely utilized to deal with different kinds of research problems in wireless communications. In this article, we investigate the secrecy rate maximization problem in a non-orthogonal multiple access network based on deep learning approach. In this non-orthogonal multiple access network, the base station intends to transmit two integrated information: a confidential information to user 1 (the strong user) and a broadcast information to user 1 and user 2. In addition, there exists an eavesdropper that intends to decode the confidential information due to the broadcast nature of radio waves. Hence, we formulate the optimization problem as a secrecy rate maximization problem. We first solve this problem by employing convex optimization technique, then we generate the training, validation, and test dataset. We propose a deep neural network–based approach to learn to optimize the resource allocations. The advantages of the proposed deep neural network are the capabilities to achieve low complexity and latency resource allocations. Simulation results are provided to show that the proposed deep neural network approach is capable of reaching near-optimal secrecy rate performance with significantly reduced computational time, when compared with the benchmark conventional approach. |
| format | Article |
| id | doaj-art-2378c0caae924c309a691db7865de364 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2022-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-2378c0caae924c309a691db7865de3642025-02-03T06:43:08ZengWileyInternational Journal of Distributed Sensor Networks1550-14772022-06-011810.1177/15501329221104330Deep learning–based resource allocation for secure transmission in a non-orthogonal multiple access networkMiao Zhang0Yao Zhang1Qian Cen2Shixun Wu3School of Information Science and Engineering, Chongqing Jiaotong University, Chongqing, ChinaAerospace New Generation Communications Co., Ltd., Chongqing, ChinaYangjiang Polytechnic, Yangjiang, ChinaSchool of Information Science and Engineering, Chongqing Jiaotong University, Chongqing, ChinaMachine learning techniques, especially deep learning algorithms have been widely utilized to deal with different kinds of research problems in wireless communications. In this article, we investigate the secrecy rate maximization problem in a non-orthogonal multiple access network based on deep learning approach. In this non-orthogonal multiple access network, the base station intends to transmit two integrated information: a confidential information to user 1 (the strong user) and a broadcast information to user 1 and user 2. In addition, there exists an eavesdropper that intends to decode the confidential information due to the broadcast nature of radio waves. Hence, we formulate the optimization problem as a secrecy rate maximization problem. We first solve this problem by employing convex optimization technique, then we generate the training, validation, and test dataset. We propose a deep neural network–based approach to learn to optimize the resource allocations. The advantages of the proposed deep neural network are the capabilities to achieve low complexity and latency resource allocations. Simulation results are provided to show that the proposed deep neural network approach is capable of reaching near-optimal secrecy rate performance with significantly reduced computational time, when compared with the benchmark conventional approach.https://doi.org/10.1177/15501329221104330 |
| spellingShingle | Miao Zhang Yao Zhang Qian Cen Shixun Wu Deep learning–based resource allocation for secure transmission in a non-orthogonal multiple access network International Journal of Distributed Sensor Networks |
| title | Deep learning–based resource allocation for secure transmission in a non-orthogonal multiple access network |
| title_full | Deep learning–based resource allocation for secure transmission in a non-orthogonal multiple access network |
| title_fullStr | Deep learning–based resource allocation for secure transmission in a non-orthogonal multiple access network |
| title_full_unstemmed | Deep learning–based resource allocation for secure transmission in a non-orthogonal multiple access network |
| title_short | Deep learning–based resource allocation for secure transmission in a non-orthogonal multiple access network |
| title_sort | deep learning based resource allocation for secure transmission in a non orthogonal multiple access network |
| url | https://doi.org/10.1177/15501329221104330 |
| work_keys_str_mv | AT miaozhang deeplearningbasedresourceallocationforsecuretransmissioninanonorthogonalmultipleaccessnetwork AT yaozhang deeplearningbasedresourceallocationforsecuretransmissioninanonorthogonalmultipleaccessnetwork AT qiancen deeplearningbasedresourceallocationforsecuretransmissioninanonorthogonalmultipleaccessnetwork AT shixunwu deeplearningbasedresourceallocationforsecuretransmissioninanonorthogonalmultipleaccessnetwork |