论文标题
可重新配置的智能表面，用于在MIMO设备到设备网络中进行干扰对齐
Reconfigurable Intelligent Surface for Interference Alignment in MIMO Device-to-Device Networks
论文作者
论文摘要
在多输入多输出（MIMO）设备到装置（D2D）网络中，干扰和缺陷渠道是实现高自由度（DOFS）的关键瓶颈。在本文中，我们提出了一个可重构的智能表面（RIS）辅助干扰对准策略，以同时减轻共同通道干扰并应对等级缺陷的通道，从而改善了干扰对准条件的可行性，从而增加了可实现的DOF。关键启用器是一种一般的低级优化方法，它通过共同设计相移和收发器矩阵来最大化可实现的DOF。为了解决耦合优化变量的独特挑战，我们通过解决固定级别和单位模量限制了最低方形子问题以及等级的提高，从而开发了一种基本结构的Riemannian追求方法。最后，为了降低计算复杂性并实现良好的DOF性能，我们开发了统一的Riemannian共轭梯度算法，以交替优化固定级的收发器矩阵和单位模量约束相位变速器，分别利用非脉冲端口示意图和复杂的圆圈歧管。数值结果证明了部署RI的有效性以及所提出的块结构的Riemannian Pusnuit方法的优越性，从可实现的DOF和可实现的总和率方面。
In multiple-input multiple-output (MIMO) device-to-device (D2D) networks, interference and rank-deficient channels are the critical bottlenecks for achieving high degrees of freedom (DoFs). In this paper, we propose a reconfigurable intelligent surface (RIS) assisted interference alignment strategy to simultaneous mitigate the co-channel interference and cope with rank-deficient channels, thereby improving the feasibility of interference alignment conditions and in turn increasing the achievable DoFs. The key enabler is a general low-rank optimization approach that maximizes the achievable DoFs by jointly designing the phase-shift and transceiver matrices. To address the unique challenges of the coupled optimization variables, we develop a block-structured Riemannian pursuit method by solving fixed-rank and unit modulus constrained least square subproblems along with rank increase. Finally, to reduce the computational complexity and achieve good DoF performance, we develop unified Riemannian conjugate gradient algorithms to alternately optimize the fixed-rank transceiver matrix and the unit modulus constrained phase shifter by exploiting the non-compact Stiefel manifold and the complex circle manifold, respectively. Numerical results demonstrate the effectiveness of deploying an RIS and the superiority of the proposed block-structured Riemannian pursuit method in terms of the achievable DoFs and the achievable sum rate.
