Design, Analysis, and Optimization of a Large Intelligent Reflecting Surface-Aided B5G Cellular Internet of Things
Guanghua Yu, Xiaoming Chen, Caijun Zhong, Derrick Wing Kwan Ng, Zhaoyang Zhang
Abstract
In this article, we apply the large intelligent reflecting surface (IRS) technique in beyond fifth-generation (B5G) cellular Internet of Things (IoT) to satisfy the requirements of massive connectivity, low power, and wide coverage. First, we design a framework for the large IRS-aided B5G cellular IoT, including channel estimation, uplink data transmission, and downlink data transmission. Then, we analyze the performance of the proposed framework, and reveal the impacts of key parameters of the large IRS on the spectral efficiency. Next, we propose a low-complexity time-length allocation algorithm to minimize the total energy consumption of B5G cellular IoT. Finally, extensive simulation results validate the accuracy of the derived theoretical expressions and the effectiveness of the proposed algorithm.