受现实因素影响的安全 IRS 辅助能量清除 NOMA

IF 2.9 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Digital Signal Processing Pub Date : 2024-09-12 DOI:10.1016/j.dsp.2024.104775
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引用次数: 0

摘要

智能反射面(IRS)辅助能量清除(ES)非正交多址(NOMA)干扰(sIEnoma)技术有望实现高频谱效率、高能效、信息安全和通信可靠性。然而,信道状态信息不完善(CSIi)、硬件不完善(HWi)、衰落严重程度和非线性能量清除(nlES)等实际不完善因素会影响 sIEnoma 的能量清除、信息安全和通信可靠性。因此,本文提出了这些不完美情况下 sIEnoma 的性能分析,以快速评估其潜力,而无需耗时的模拟。结果表明,不完善因素(HWi、CSIi、衰落严重程度、nlES)和规范(IRS、NOMA)对 sIEnoma 的性能有显著影响。然而,适当采用功率分配系数、所需数据速率、HWi 水平和时间切换系数可以阻止 sIEnoma 完全中断。此外,通过正确配置系统参数,可以实现最佳指标。此外,所提出的 sIEnoma 优于其基线(IRS 辅助 ES 干扰正交多址)。
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Secure IRS-assisted energy scavenging NOMA impaired by realistic factors

Intelligent reflective surface (IRS)-assisted energy scavenging (ES) non-orthogonal multiple access (NOMA) with jamming (sIEnoma) is promising for high spectral efficiency, energy efficiency, information security, and communication reliability. Nonetheless, practical imperfections, such as channel state information imperfection (CSIi), hardware imperfection (HWi), fading severity, and nonlinear energy scavenging (nlES) impact scavenged energy, information security, and communication reliability of sIEnoma. Accordingly, this paper proposes performance analysis for sIEnoma under these imperfections to quickly assess its potentials without time-consuming simulations. Results show that imperfections (HWi, CSIi, fading severity, nlES) and specifications (IRS, NOMA) significantly impact performance of sIEnoma. Nevertheless, proper adoption of power splitting factor, desired data rate, HWi level, and time switching coefficient can hinder complete outage in sIEnoma. Additionally, optimal metrics are achieved by configuring system parameters properly. Moreover, the proposed sIEnoma outperforms its baseline (IRS-assisted ES orthogonal multiple access with jamming).

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来源期刊
Digital Signal Processing
Digital Signal Processing 工程技术-工程:电子与电气
CiteScore
5.30
自引率
17.20%
发文量
435
审稿时长
66 days
期刊介绍: Digital Signal Processing: A Review Journal is one of the oldest and most established journals in the field of signal processing yet it aims to be the most innovative. The Journal invites top quality research articles at the frontiers of research in all aspects of signal processing. Our objective is to provide a platform for the publication of ground-breaking research in signal processing with both academic and industrial appeal. The journal has a special emphasis on statistical signal processing methodology such as Bayesian signal processing, and encourages articles on emerging applications of signal processing such as: • big data• machine learning• internet of things• information security• systems biology and computational biology,• financial time series analysis,• autonomous vehicles,• quantum computing,• neuromorphic engineering,• human-computer interaction and intelligent user interfaces,• environmental signal processing,• geophysical signal processing including seismic signal processing,• chemioinformatics and bioinformatics,• audio, visual and performance arts,• disaster management and prevention,• renewable energy,
期刊最新文献
Editorial Board Continuous discrete minimum error entropy Kalman filter in non-Gaussian noises system Interpretable ADMM-CSNet for interrupted sampling repeater jamming suppression Self-learning based joint multi image super-resolution and sub-pixel registration Dynamic mode decomposition-based technique for cross-term suppression in the Wigner-Ville distribution
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