Secrecy analysis and learning-based optimization of cooperative NOMA SWIPT systems
Jameel, F., Khan, W. U., Chang, Z., Ristaniemi, T., & Liu, J. (2019). Secrecy analysis and learning-based optimization of cooperative NOMA SWIPT systems. In 2019 IEEE International Conference on Communications Workshops (ICC Workshops 2019). IEEE. IEEE International Conference on Communications. https://doi.org/10.1109/ICCW.2019.8756894
Published inIEEE International Conference on Communications
Non-orthogonal multiple access (NOMA) is considered to be one of the best candidates for future networks due to its ability to serve multiple users using the same resource block. Although early studies have focused on transmission reliability and energy efficiency, recent works are considering cooperation among the nodes. The cooperative NOMA techniques allow the user with a better channel (near user) to act as a relay between the source and the user experiencing poor channel (far user). This paper considers the link security aspect of energy harvesting cooperative NOMA users. In particular, the near user applies the decode-and-forward (DF) protocol for relaying the message of the source node to the far user in the presence of an eavesdropper. Moreover, we consider that all the devices use power-splitting architecture for energy harvesting and information decoding. We derive the analytical expression of intercept probability. Next, we employ deep learning based optimization to find the optimal power allocation factor. The results show the robustness and superiority of deep learning optimization over conventional iterative search algorithm. ...
Parent publication ISBN978-1-72812-373-8
ConferenceIEEE International Conference on Communications Workshops
Is part of publication2019 IEEE International Conference on Communications Workshops (ICC Workshops 2019)
ISSN Search the Publication Forum1550-3607
Publication in research information system
MetadataShow full item record
Additional information about fundingThis work is partially supported by the National Key R & D Plan (2017YFC0803403), the National Natural Science Foundation of China (61371188) and the Fundamental Research Funds of Shandong University (2018GN051).
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