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- W3048384961 abstract "Non-orthogonal multiple access (NOMA) and backscatter communications are considered to be promising technologies due to their applications in large-scale Internet-of-things (IoT) networks for ensuring low-powered and spectral-efficient communication. However, massive connectivity of IoT devices may result in compromising the link security, resulting in information leakage to eavesdroppers. To solve this issue, we consider a multi-cell backscatter network where a base station (BS) in each cell communicates to cellular users using the power-domain NOMA technique. A backscatter node in each cell also receives the superimposed signal from BS, utilizes this signal to modulate data and, then, retransmit it to nearby cellular user in the presence of multiple eavesdroppers. The eavesdroppers in the vicinity may try to overhear the transmission of the backscatter node due to the broadcast nature of the wireless network. Therefore, we investigate an optimization problem to maximize the secrecy rate of the NOMA-enabled multi-cell backscatter network. In particular, we optimize the reflection coefficient of the backscatter node in the presence of multiple eavesdroppers in each cell. The optimization problem is formulated as a convex problem that is subjected to the maximum reflection coefficient of the backscatter node. To obtain an optimal solution, we exploit Karush-Kuhn-Tucker conditions where the Lagrangian multipliers are updated by the sub-gradient method. We also present the secrecy maximization problem under traditional time division multiple access (TDMA) for the sake of comparison. Finally, the results are obtained using the Monte Carlo simulation which demonstrates that the proposed NOMA scheme significantly outperforms the traditional TDMA scheme." @default.
- W3048384961 created "2020-08-18" @default.
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- W3048384961 date "2020-07-01" @default.
- W3048384961 modified "2023-09-23" @default.
- W3048384961 title "Secure Backscatter Communications in Multi-Cell NOMA Networks: Enabling Link Security for Massive IoT Networks" @default.
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- W3048384961 doi "https://doi.org/10.1109/infocomwkshps50562.2020.9162938" @default.
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