Design, Analysis, and Optimization of a Large Intelligent Reflecting Surface-Aided B5G Cellular Internet of Things

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.

I. Introduction

In the past few years, Internet of Things (IoT) has been widely used in a variety of fields, e.g., industry, agriculture, traffic, and medicine [1]–[3]. In this context, the IoT network exhibits some new characteristics, i.e., massive connectivity, low power, and wide coverage [4]. First, the number of IoT devices has an explosive increasement. It is predicted that the number of IoT devices will reach hundreds of billions in 2030 [5]. Second, most IoT devices are low-power nodes. It is expected that the battery of the IoT device can be used even for ten years. Third, IoT devices are distributed over a large range. It is obvious that the current 5G narrow-band IoT (NB-IoT) cannot satisfy these requirements. Hence, it is time to design beyond 5G (B5G) cellular IoT by exploiting new wireless techniques [6], [7].