Backscatter-assisted Non-orthogonal multiple access network for next generation communication

IF 1.1 4区工程技术 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC

IET Signal Processing Pub Date : 2023-04-24 DOI:10.1049/sil2.12211

Ximing Xie, Zhiguo Ding

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引用次数: 0

Abstract

Non-orthogonal multiple access (NOMA) technique introduces spectrum cooperation among different users and devices, which improves spectrum efficiency significantly. Energy-limited devices benefit from the backscatter (BAC) technique to transmit signals without extra energy consumption. The combination of NOMA and BAC provides a promising solution for Internet of Things (IoT) networks, where massive devices simultaneously transmit and receive signals. This study investigates a system model with two NOMA downlink users and an uplink device. The aim is to maximise the data rate of the uplink device by optimising the power allocation coefficient and the backscattering coefficient. Meanwhile the quality of service requirements of two NOMA users are guaranteed. The closed-form solution of two optimisation variables is derived, and an alternating algorithm is also proposed to solve the formulated optimisation problem efficiently. The proposed system verifies the feasibility of IoT devices being added into existing networks and provides a promising solution for wireless communication networks in the future.

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用于下一代通信的后向散射辅助非正交多址网络

非正交多址（NOMA）技术引入了不同用户和设备之间的频谱协作，显著提高了频谱效率。能量有限的设备受益于反向散射（BAC）技术，可以在没有额外能量消耗的情况下传输信号。NOMA和BAC的结合为物联网（IoT）网络提供了一个很有前途的解决方案，在物联网网络中，大量设备可以同时传输和接收信号。本研究研究了一个具有两个NOMA下行链路用户和一个上行链路设备的系统模型。其目的是通过优化功率分配系数和反向散射系数来最大化上行链路设备的数据速率。同时保证了两个NOMA用户的服务质量要求。导出了两个优化变量的闭式解，并提出了一种交替算法来有效地求解公式化的优化问题。所提出的系统验证了将物联网设备添加到现有网络中的可行性，并为未来的无线通信网络提供了一个有前景的解决方案。

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来源期刊

IET Signal Processing 工程技术-工程：电子与电气

CiteScore

3.80

自引率

5.90%

发文量

审稿时长

9.5 months

期刊介绍： IET Signal Processing publishes research on a diverse range of signal processing and machine learning topics, covering a variety of applications, disciplines, modalities, and techniques in detection, estimation, inference, and classification problems. The research published includes advances in algorithm design for the analysis of single and high-multi-dimensional data, sparsity, linear and non-linear systems, recursive and non-recursive digital filters and multi-rate filter banks, as well a range of topics that span from sensor array processing, deep convolutional neural network based approaches to the application of chaos theory, and far more. Topics covered by scope include, but are not limited to: advances in single and multi-dimensional filter design and implementation linear and nonlinear, fixed and adaptive digital filters and multirate filter banks statistical signal processing techniques and analysis classical, parametric and higher order spectral analysis signal transformation and compression techniques, including time-frequency analysis system modelling and adaptive identification techniques machine learning based approaches to signal processing Bayesian methods for signal processing, including Monte-Carlo Markov-chain and particle filtering techniques theory and application of blind and semi-blind signal separation techniques signal processing techniques for analysis, enhancement, coding, synthesis and recognition of speech signals direction-finding and beamforming techniques for audio and electromagnetic signals analysis techniques for biomedical signals baseband signal processing techniques for transmission and reception of communication signals signal processing techniques for data hiding and audio watermarking sparse signal processing and compressive sensing Special Issue Call for Papers: Intelligent Deep Fuzzy Model for Signal Processing - https://digital-library.theiet.org/files/IET_SPR_CFP_IDFMSP.pdf