Artificial Noise Aided Secure Cognitive Beamforming for Cooperative MISO-NOMA Using SWIPT
Fuhui Zhou, Zheng Chu, Haijian Sun, Rose Qingyang Hu, and Lajos Hanzo

TL;DR
This paper proposes a secure cooperative beamforming scheme using artificial noise in a cognitive NOMA network with SWIPT, considering practical non-linear energy harvesting models and channel uncertainties, demonstrating improved security and power efficiency.
Contribution
It introduces a novel artificial noise aided beamforming approach for secure NOMA with SWIPT, incorporating practical energy harvesting models and robust optimization under CSI errors.
Findings
The proposed scheme achieves secure communication in cognitive NOMA networks.
NOMA outperforms orthogonal multiple access in power efficiency.
The cost function algorithm outperforms SDR-based solutions.
Abstract
Cognitive radio (CR) and non-orthogonal multiple access (NOMA) have been deemed two promising technologies due to their potential to achieve high spectral efficiency and massive connectivity. This paper studies a multiple-input single-output NOMA CR network relying on simultaneous wireless information and power transfer (SWIPT) conceived for supporting a massive population of power limited battery-driven devices. In contrast to most of the existing works, which use an ideally linear energy harvesting model, this study applies a more practical non-linear energy harvesting model. In order to improve the security of the primary network, an artificial-noise-aided cooperative jamming scheme is proposed. The artificial-noise-aided beamforming design problems are investigated subject to the practical secrecy rate and energy harvesting constraints. Specifically, the transmission power…
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Taxonomy
TopicsEnergy Harvesting in Wireless Networks · Advanced Wireless Communication Technologies · Wireless Communication Security Techniques
