A4 Article in conference proceedings
Secrecy analysis and learning-based optimization of cooperative NOMA SWIPT systems (2019)


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


JYU authors or editors


Publication details

All authors or editors: Jameel, Furqan; Khan, Wali Ullah; Chang, Zheng; Ristaniemi, Tapani; Liu, Ju

Parent publication: 2019 IEEE International Conference on Communications Workshops (ICC Workshops 2019)

Place and date of conference: Shanghai, China, 20.-24.5.2019

ISBN: 978-1-72812-373-8

Journal or series: IEEE International Conference on Communications

ISSN: 1550-3607

eISSN: 1938-1883

Publication year: 2019

Publisher: IEEE

Publication country: United States

Publication language: English

DOI: https://doi.org/10.1109/ICCW.2019.8756894

Publication open access: Not open

Publication channel open access:

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/65375

Publication is parallel published: https://arxiv.org/abs/1907.05753


Abstract

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.


Keywords: wireless data transmission; 5G technology; data security; optimisation; machine learning

Free keywords: Decode-and-forward (DF), Deep learning, Nonorthogonal multiple access (NOMA), Power-splitting


Contributing organizations


Ministry reporting: Yes

Reporting Year: 2019

JUFO rating: 1


Last updated on 2021-10-06 at 13:52