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Secure Uplink IM-OFDMA With Artificial IQ Imbalance

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submitted on 2024-02-14, 04:49 and posted on 2024-02-14, 04:49 authored by Ozgur Alaca, Saud Althunibat, Serhan Yarkan, Scott L. Miller, Khalid A. Qaraqe

This study proposes a novel secure uplink index modulation-based orthogonal frequency division multiple access (IM-OFDMA) systems using artificial in-phase and quadrature imbalance (A-IQI). The fact of the distinct A-IQI induced at the users’ end and the unpredictable allocation of users based on confidential data brings a different perspective on physical layer security. Accordingly, in this study, the non-identical effect of A-IQI is exploited to provide a physical-layer security scheme for the IM-OFDMA systems. Specifically, the estimation method is created with preamble data in order to achieve each user’s unique A-IQI information from a legitimate receiver. The A-IQI will help to secure the transmitted data against a potential eavesdropper, and hence, improving data confidentiality. The proposed scheme’s performance is analytically evaluated by deriving closed-form expressions of the average bit error rate (BER) at both the base station and the eavesdropper. The image rejection ratios of A-IQI and practical IQI are provided for IM-OFDMA to indicate the region of intentionally generated A-IQI. Under the different system model specifications of IM-OFDMA, the analytical results are provided with simulation results obtained using the Monte Carlo simulation method. Further, the proposed secure scheme is compared with the state-of-the-art artificial noise method. Results reveal a significant improvement in the BER at the base station, accompanied by severe degradation in the BER at the eavesdropper.

Other Information

Published in: IEEE Access
License: http://creativecommons.org/licenses/by/4.0
See article on publisher's website: https://dx.doi.org/10.1109/access.2023.3284687

Funding

Open Access funding provided by the Qatar National Library.

History

Language

  • English

Publisher

IEEE

Publication Year

  • 2023

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License

Institution affiliated with

  • Texas A&M University at Qatar