Full duplex reconfigurable intelligent surfaces system relying on NOMA and wireless power transfer

Minh-Sang Van Nguyen, Dinh-Thuan Do,Phu Tran Tin, Agbotiname Lucky Imoize,Vinoth Babu Kumaravelu

Wireless Networks(2024)

引用 0|浏览0
暂无评分
摘要
In this article, we analyze the downlink transmission of a wirelessly powered hybrid cooperative non-orthogonal multiple access (C-NOMA) system relying on reconfigurable intelligent surfaces (RIS), where self-interference related to full-duplex (FD) and power efficiency are also considered. The network contains a single-antenna base station (BS), a RIS with many reflecting elements, a power beacon, and two users that can function in FD in comparison with the benchmark, i.e. half-duplex mode. In the proposed system, there are two communication paths from the BS to the far-user either through the RIS or via a near-user employing energy-harvesting from a power beacon. To comprehend the performance of the proposed system, we study and compare the outage probability under various parameters of interest such as power allocation coefficients, energy harvesting coefficients, number of RIS reflecting elements, and transmission rates. We aim to show the trade-off between these main parameters and system outage performance. Moreover, to highlight the advantages of the wireless-powered RIS-aided CNOMA system, we compare it against with benchmark, i.e. a wireless-powered RIS-aided cooperative orthogonal multiple access (C-OMA) system. All derived closed-form outage expressions are verified by employing Monte-Carlo simulations. Numerical results show that: (1) wireless powered RIS-aided FD C-NOMA is superior to the wireless-powered RIS-aided FD C-OMA in terms of outage probability in the high signal-to-noise ratio (SNR) region; and (2) in delay-to-tolerant transmission scenarios, the energy efficiency of the proposed wireless powered RIS FD C-NOMA outperforms the wireless powered RIS FD C-OMA in the high SNR region.
更多
查看译文
关键词
Reconfigurable intelligent surfaces,Full duplex,Outage probability
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要