Robust Transmission Design for RIS-Aided Wireless Communication With Both Imperfect CSI and Transceiver Hardware Impairments

Reconfigurable intelligent surface (RIS) has recently been regarded as a potential technique to enhance the performance of wireless communication systems by creating additional communication links. However, it is almost impossible to get the perfect channel state information (CSI) from the base station (BS) to the Internet of Things Devices (IoTDs) and the RIS-related channels. Furthermore, residual transceiver hardware impairments inevitably affect the performance of wireless communication systems. Hence, we study the robust design for an RIS-aided wireless communication system based on the imperfect CSI and hardware impairments. Minimizing the power consumption of BS is formulated by ensuring the minimum signal-to-interference-plus-noise ratio (SINR) demands of the IoTDs and the unit-modulus constraints of the RIS. Specifically, after approximating the nonconvex constraints by using the S-procedure and the successive convex approximation (SCA) methods, we adopt the block coordinate descent (BCD) technique to iteratively optimize one set of variables while keeping the other variables fixed in various channel uncertainty scenarios. Simulation results demonstrate that the influence of transceiver hardware impairments can be effectively decreased by deploying RIS even with channel uncertainty, which is more advantageous than increasing the number of BS’s antennas.

I. Introduction

According to the report that was put out by Cisco Systems, Inc., in March 2020, the number of network equipments will attain 29.3 billions by 2023, in which half of them are mobile communication equipments [1]. This inevitable increase brings rapid growth of mobile data traffic and multiple application interactions; thus, it is imperative to enhance the performance of wireless communication systems by introducing new technologies. Though the 5th-generation (5G) wireless communication has emerged as a hopeful approach to meet these requirements, there is no single way that can assist all the application requirements [2]. New low-cost and energy-efficient techniques are eagerly proposed to improve the radio propagation environment of Internet of Things (IoT) systems.