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.
翻译:在本文中,我们分别制作、分析和优化多用户和多顺序反射(MMOR)智能反射表面网络(IRS),我们首先获得适用于IRS/反射器的任意反射时间、大小和数目的完整MUMORIRS网络模型,在封闭式功能中,用IRS实现总和上限的最佳条件,以及实现无干扰传输的分析条件。利用这一最佳条件,我们获得IRS网络中具有不同网络表层的MUMOR总和率上限,其中考虑了线性图(LG)、完整图(CG)和空图(NG)的表层。模拟结果证实了我们的理论和推理,并表明不同网络表层的总和率上限在K-egy IRS的改进之下。