Multi-ARIS Backscatter Enabled Downlink NOMA Communication for Cognitive Radio Systems

被引:1
作者
Han, Shuai [1 ]
Sun, Zeyang [1 ]
Xu, Sai [2 ]
Xie, Dan [1 ]
Li, Cheng [3 ]
机构
[1] Harbin Inst Technol, Sch Elect & Informat Engn, Harbin 150001, Heilongjiang, Peoples R China
[2] Southeast Univ, Natl Mobile Commun Res Lab, Nanjing 211111, Peoples R China
[3] Mem Univ, Elect & Comp Engn Fac Engn & Appl Sci, St John, NF A1B 3X5, Canada
基金
中国国家自然科学基金;
关键词
Active reconfigurable intelligent surface; Backscatter; non-orthogonal multiple access; cognitive radio; weighted sum rate; backscatter; CHANNEL ESTIMATION; INDEX MODULATION; POWER ALLOCATION; SUM RATE; INTELLIGENT; NETWORKS; ACCESS; OPTIMIZATION; CHALLENGES; DESIGN;
D O I
10.1109/TCCN.2024.3415623
中图分类号
TN [电子技术、通信技术];
学科分类号
0809 ;
摘要
This paper investigates an active reconfigurable intelligent surface (ARIS) backscatter communication (BackCom) enabled downlink NOMA scheme in a cognitive radio (CR) system. Specifically, a dedicated primary beacon (PB) is deployed to serve the primary user while providing carrier signal for ARIS-BackCom units, which act as secondary transmitters (STs) to communicate with secondary users (SUs). Notably, the deployment of multiple ARIS-BackCom devices not only reduces the radio frequency (RF) component costs in the secondary network, but also provides multiple reflective link array gains, and overcomes the double-fading phenomenon effect. Based on the established system framework, we formulate the joint optimization problem of the beamforming vectors at the PB and the STs to maximize the weighted sum rate of the SUs while ensuring quality of service for the PU. The highly-coupled non-convex problem is decomposed into three sub-problems, which can be effectively addressed jointly using fractional programming, Lagrangian dual transform, difference-of-convex, and successive convex optimization methods. Finally, the comprehensive simulation results demonstrate that, in comparison with the traditional passive RIS-BackCom network, the proposed scheme significantly boosts the network's weighted sum rate (WSR) by a minimum of 60%.
引用
收藏
页码:408 / 422
页数:15
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