发表机构
University of the Witwatersrand（金山大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对底层认知无线电中RIS辅助反向散射NOMA网络，联合优化波束成形与RIS反射矩阵以最大化加权和速率，提出交替优化算法，并推导闭式功率分配，实现反向散射与NOMA的平衡。
AI 中文摘要
本文研究了一个可重构智能表面（RIS）辅助的反向散射非正交多址接入（NOMA）网络，该网络作为底层认知无线电网络的次级系统运行。一个多天线基站通过RIS服务于多个用户簇，每个簇包含一个反向散射设备（BD）。我们通过联合优化发射波束成形器和RIS反射矩阵，在功率预算、干扰温度限制和最小速率要求的约束下，最大化次级网络的加权和速率。该非凸问题通过交替优化求解，采用半定松弛和序贯秩一约束松弛。干扰归一化的用户排序使得连续干扰消除约束变得冗余。推导出闭式等速率功率分配，其与仿真结果高度吻合，且仅损失约4.5%的和速率。支持BD相对于无BD设计使NOMA和速率降低8.7%，而较大的BD反射系数可提高反向散射速率，且NOMA损失很小。
英文摘要
This paper investigates a reconfigurable intelligent surface (RIS)-assisted backscatter non-orthogonal multiple access (NOMA) network operating as the secondary system of an underlay cognitive radio network. A multi-antenna base station serves several user clusters through the RIS, each containing a backscatter device (BD). We maximise the weighted sum rate of the secondary network by jointly optimising the transmit beamformers and the RIS reflection matrix, subject to the power budget, the interference-temperature limit and minimum rate requirements. The non-convex problem is solved by alternating optimisation, using semidefinite relaxation and sequential rank-one constraint relaxation. Interference-normalised user ordering makes the successive interference cancellation constraints redundant. A closed-form equal-rate power split is derived; it agrees closely with simulation and costs about 4.5% of the sum rate. Supporting the BDs reduces the NOMA sum rate by 8.7% relative to a no-BD design, while a larger BD reflection coefficient raises the backscatter rate with little NOMA loss.
