Performance analysis of non-linear energy harvesting cognitive two-way relay network based on NOMA

IF 2.9 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Yi Luo , Chuanying Xu , Bingzhen Li , Jian Dong , Lijia Wang , Xiangxiang Li , Qingyun Liu , Ying Lin
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引用次数: 0

Abstract

In this paper, we propose an underlay non-orthogonal multiple access (NOMA) network with an energy harvesting (EH) based two-way relay (TWR) for wireless sensor networks. The two NOMA sensors exchange information simultaneously through the TWR which harvests energy from the radio frequency signal of a power beacon by using a piecewise linear EH model. Considering instantaneous channel state information (CSI) and statistical CSI available, respectively, we derive approximate analytical expressions for the outage probabilities of the two NOMA sensors and the delay-limited sum-throughput of the secondary network. It is seen that the optimal selection of target end-to-end rates, power allocation coefficients, and EH time ratio is crucial for maximizing sum-throughput. Extensive Monte Carlo simulations are performed to corroborate our analytical results.
基于NOMA的非线性能量收集认知双向中继网络性能分析
本文提出了一种基于能量收集(EH)的双向中继(TWR)的无线传感器网络底层非正交多址(NOMA)网络。两个NOMA传感器通过TWR同时交换信息,TWR利用分段线性EH模型从功率信标的射频信号中获取能量。在考虑瞬时信道状态信息(CSI)和统计CSI可用的情况下,我们分别导出了两个NOMA传感器的中断概率和二次网络的延迟限制总吞吐量的近似解析表达式。可以看出,目标端到端速率、功率分配系数和EH时间比的最佳选择对于最大限度地提高总吞吐量至关重要。进行了大量的蒙特卡罗模拟来证实我们的分析结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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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