论文标题
具有多阶反射效果的智能反射表面网络：系统建模和关键界限
Intelligent Reflecting Surface Networks with Multi-Order-Reflection Effect: System Modelling and Critical Bounds
论文作者
论文摘要
在本文中，我们建模，分析和优化多用户和多阶反射（Mumor）智能反射表面（IRS）网络。我们首先得出一个完整的Mumor IRS网络模型，适用于IRS/反射器的反射，大小和数量的任意时间。在封闭形式功能中与一个IRS实现总和上限的最佳条件和实现无干扰传输的分析条件的最佳条件。利用这种最佳条件，我们获得了具有不同网络拓扑的IRS网络的Mumor Sum-rate上限，其中考虑了线性图（LG），完整图（CG）和NULL Graph（NG）拓扑。仿真结果验证了我们的理论和推导，并证明了不同网络拓扑的总和上限在K型IRS的情况下在K折的下方改进。
In this paper, we model, analyze and optimize the multi-user and multi-order-reflection (MUMOR) intelligent reflecting surface (IRS) networks. We first derive a complete MUMOR IRS network model applicable for the arbitrary times of reflections, size and number of IRSs/reflectors. The optimal condition for achieving sum-rate upper bound with one IRS in a closed-form function and the analytical condition to achieve interference-free transmission are derived, respectively. Leveraging this optimal condition, we obtain the MUMOR sum-rate upper bound of the IRS network with different network topologies, where the linear graph (LG), complete graph (CG) and null graph (NG) topologies are considered. Simulation results verify our theories and derivations and demonstrate that the sum-rate upper bounds of different network topologies are under a K-fold improvement given K-piece IRS.
