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Reconfigurable Intelligent Surface Empowered Downlink Non-Orthogonal Multiple Access
IEEE Transactions on Communications ( IF 8.3 ) Pub Date : 2021-03-17 , DOI: 10.1109/tcomm.2021.3066587
Min Fu , Yong Zhou , Yuanming Shi , Khaled B. Letaief

Power-domain non-orthogonal multiple access (NOMA) has become a promising technology to exploit the new dimension of the power domain to enhance the spectral efficiency of wireless networks. However, most existing NOMA schemes rely on the strong assumption that users’ channel gains are quite different, which may be invalid in practice. To unleash the potential of power-domain NOMA, we propose a reconfigurable intelligent surface (RIS)-empowered NOMA scheme to introduce desirable channel gain differences among the users by adjusting the phase shifts at the RIS. Our goal is to minimize the total transmit power by jointly optimizing the beamforming vectors at the base station, the phase-shift matrix at the RIS, and user ordering. To address challenge due to the highly coupled optimization variables, we present an alternating optimization framework to decompose the non-convex bi-quadratically constrained quadratic problem under a specific user ordering into two rank-one constrained matrices optimization problems via matrix lifting. To accurately detect the feasibility of the non-convex rank-one constraints and improve performance by avoiding early stopping in the alternating optimization procedure, we equivalently represent the rank-one constraint as the difference between nuclear norm and spectral norm. A difference-of-convex (DC) algorithm is further developed to solve the resulting DC programs via successive convex relaxation, followed by establishing the convergence of the proposed DC-based alternating optimization method. We further propose an efficient user ordering scheme with closed-form expressions, considering both the channel conditions and users’ target data rates. Simulation results validate the ability of an RIS in enlarging the channel-gain difference when the users’ original channel conditions are similar and the superiority of the proposed DC-based alternating optimization method in reducing the total transmit power.

中文翻译:

可重构智能表面赋能下行非正交多址接入

功率域非正交多址(NOMA)已成为一种很有前途的技术,可以利用功率域的新维度来提高无线网络的频谱效率。然而,大多数现有的 NOMA 方案都依赖于用户的信道增益差异很大的强假设,这在实践中可能是无效的。为了释放功率域 NOMA 的潜力,我们提出了一种可重构智能表面 (RIS) 授权的 NOMA 方案,通过调整 RIS 的相移来在用户之间引入理想的信道增益差异。我们的目标是通过联合优化基站的波束成形矢量、RIS 的相移矩阵和用户排序来最小化总发射功率。为了应对高度耦合的优化变量带来的挑战,我们提出了一个交替优化框架,通过矩阵提升将特定用户排序下的非凸双二次约束二次问题分解为两个秩一约束矩阵优化问题。为了准确检测非凸秩一约束的可行性并通过避免交替优化过程中的提前停止来提高性能,我们将秩一约束等效表示为核范数和谱范数之间的差异。进一步开发了一种凸差 (DC) 算法,通过连续凸松弛来求解产生的 DC 程序,然后建立所提出的基于 DC 的交替优化方法的收敛性。我们进一步提出了一种具有封闭形式表达式的高效用户排序方案,考虑信道条件和用户的目标数据速率。仿真结果验证了RIS在用户原始信道条件相似时扩大信道增益差异的能力,以及所提出的基于DC的交替优化方法在降低总发射功率方面的优越性。
更新日期:2021-03-17
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