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物联网中继系统的智能表面赋能与用户配对:中断概率和遍历容量性能

Empowering Intelligent Surfaces and User Pairing for IoT Relaying Systems: Outage Probability and Ergodic Capacity Performance.

作者信息

Dang Huu-Phuc, Nguyen Minh-Sang Van, Do Dinh-Thuan, Nguyen Minh-Hoa, Pham Minh-Triet, Kim Anh-Tuan

机构信息

Electrical-Electronics Department, School of Engineering and Technology, Tra Vinh University, Tra Vinh 87000, Vietnam.

Faculty of Electronics Technology, Industrial University of Ho Chi Minh City (IUH), Ho Chi Minh City 70000, Vietnam.

出版信息

Sensors (Basel). 2022 Aug 31;22(17):6576. doi: 10.3390/s22176576.

DOI:10.3390/s22176576
PMID:36081034
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9460030/
Abstract

The evolution of Internet of Things (IoT) networks has been studied owing to the associated benefits in useful applications. Although the evolution is highly helpful, the increasing day-to-day demands of mobile users have led to immense requirements for further performance improvements such as efficient spectrum utilization, massive device connectivity, and high data rates. Fortunately, reconfigurable intelligent surfaces (RIS) and non-orthogonal multiple access (NOMA) techniques have recently been introduced as two possible current-generation emerging technologies with immense potential of addressing the above-mentioned issues. In this paper, we propose the integration of RIS to the existing techniques (i.e., NOMA and relaying) to further enhance the performance for mobile users. We focus on a performance analysis of two-user group by exploiting two main performance metrics including outage probability and ergodic capacity. We provide closed-form expressions for both performance metrics to highlight how NOMA-aided RIS systems provide more benefits compared with the benchmark based on traditional orthogonal multiple access (OMA). Monte-Carlo simulations are performed to validate the correctness of obtained expressions. The simulations show that power allocation factors assigned to two users play a major role in the formation of a performance gap among two users rather than the setting of RIS. In particular, the strong user achieves optimal outage behavior when it is allocated 35% transmit power.

摘要

由于物联网(IoT)网络在实用应用中具有相关优势,其演进过程已得到研究。尽管这种演进非常有帮助,但移动用户日益增长的日常需求导致对进一步性能提升有巨大需求,例如高效频谱利用、大规模设备连接和高数据速率。幸运的是,可重构智能表面(RIS)和非正交多址接入(NOMA)技术最近作为两种可能的当代新兴技术被引入,它们具有解决上述问题的巨大潜力。在本文中,我们提议将RIS集成到现有技术(即NOMA和中继)中,以进一步提高移动用户的性能。我们通过利用包括中断概率和遍历容量在内的两个主要性能指标,专注于对两用户组的性能分析。我们为这两个性能指标提供了闭式表达式,以突出与基于传统正交多址接入(OMA)的基准相比,NOMA辅助的RIS系统如何提供更多优势。进行了蒙特卡罗模拟以验证所得表达式的正确性。模拟表明,分配给两个用户的功率分配因子在两个用户之间性能差距的形成中起主要作用,而不是RIS的设置。特别是,当强用户被分配35%的发射功率时,它实现了最优的中断行为。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc44/9460030/0df16c3209fe/sensors-22-06576-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc44/9460030/5e4e2af4095e/sensors-22-06576-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc44/9460030/6291e7553fde/sensors-22-06576-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc44/9460030/c3b5d1681f20/sensors-22-06576-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc44/9460030/74e060e90371/sensors-22-06576-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc44/9460030/491fdca0d425/sensors-22-06576-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc44/9460030/d944095f1cc4/sensors-22-06576-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc44/9460030/486f6e1a9104/sensors-22-06576-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc44/9460030/0df16c3209fe/sensors-22-06576-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc44/9460030/5e4e2af4095e/sensors-22-06576-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc44/9460030/6291e7553fde/sensors-22-06576-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc44/9460030/c3b5d1681f20/sensors-22-06576-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc44/9460030/74e060e90371/sensors-22-06576-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc44/9460030/491fdca0d425/sensors-22-06576-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc44/9460030/d944095f1cc4/sensors-22-06576-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc44/9460030/486f6e1a9104/sensors-22-06576-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc44/9460030/0df16c3209fe/sensors-22-06576-g008.jpg

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