利用非正交多址同时进行无线信息和功率传输的全双工合作中继系统

IF 2.9 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Digital Signal Processing Pub Date : 2024-10-28 DOI:10.1016/j.dsp.2024.104817
Huu Q. Tran , Lam Hoang Kham , Ho Van Khuong
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引用次数: 0

摘要

在这项研究中,我们提出了一种基于合作式非正交多址接入(NOMA)的系统模型,用于全双工(FD)通信框架内的同步无线信息和功率传输(SWIPT)。我们研究了两种协议--时间切换协议(TSR)和功率分配协议(PSR)--旨在适应延迟容忍传输(DTT)和延迟限制传输(DLT),从而改进数据处理和能量收集(EH)。我们提出了关键性能指标的明确公式,如能效、遍历率、吞吐量和中断概率。这些性能指标在多种规格中进行了全面评估,包括用户间分离、所需频谱效率、EH 效率、中高信噪比情况下的时间和功率分配比例。结果表明,EH 效率得到了提高,从而改善了传输可靠性。重要的是,事实证明,拟议系统模型中的 NOMA 比传统的正交多址接入要好得多。
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Full-duplex cooperative relaying systems for simultaneous wireless information and power transfer with non-orthogonal multiple access
In this research, we propose a system model based on cooperative non-orthogonal multiple access (NOMA) for simultaneous wireless information and power transfer (SWIPT) within a full-duplex (FD) communication framework. We investigate two protocols - time switching protocol (TSR) and power splitting protocol (PSR) - designed to accommodate delay-tolerant-transmission (DTT) as well as delay-limited-transmission (DLT), thereby improving data processing and energy harvesting (EH). We present explicit formulas for pivotal performance measures such as energy efficiency, ergodic rate, throughput, and outage probability. These performance measures are thoroughly evaluated in numerous specifications, encompassing inter-user separation, required spectral efficiency, EH efficiency, time and power splitting ratios in moderate-to-high signal-to-noise ratio scenarios. The results expose improved EH efficiency, hence meliorated transmission reliability. Importantly, NOMA in the proposed system model is proved to be considerably better than traditional orthogonal multiple access.
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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,
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