A Comprehensive Review of Intelligent Reflecting Surfaces from Hardware to Industrial Integration and Future Directions

Abstract

An Intelligent Reflecting Surface (IRS) has emerged as a key solution to performance bottlenecks in wireless communication. Its ability to combat multipath fading and improve signal and energy efficiencies has made it relevant to various industry applications, including the Internet of Things (IoT), smart manufacturing, cognitive radio, radar, and Multiple-Input Multiple-Output (MIMO) systems. This paper presents a comprehensive review of the IRS’s structure and hardware requirements, channel estimation, optimization methods, and key applications to enable readers to understand how the IRS operates, its benefits, and some of the challenges involved in its application. The structure and hardware requirements are important to understand as they dictate the material composition, number, and arrangement of reflecting elements, and their reconfigurability. Channel State Information (CSI) plays a crucial role in optimized transmission as it gives information on the channel conditions, enabling users to tailor their transmission accordingly. In this work, all scholarly papers related to the IRS published between 2010-2024 were considered, sampled, and categorized based on the key themes. An analysis of the hardware and architecture reveals that transceiver hardware imperfections significantly affect IRS optimization and should be considered. While several channel estimation techniques offer comparable benefits, accuracy turns out to be the most important factor to consider. Further, results show that flexibility and inference accuracy make machine learning techniques superior to other optimization methods. Still, challenges remain in relation to IRS standardization, privacy concerns, and handover techniques that ought to be addressed for future industrial integration.