Number Configuration for RIS-Aided Systems: Opportunities and Challenges

Reconfigurable intelligent surface (RIS), a recently emerging technology in wireless communications, has been gradually attracting widespread attention. Generally, a high array gain can be achieved by increasing the number of reflecting elements. However, the number of elements is limited by cost, overhead, deployment environments, power consumption, etc. In particular, the cost and power consumption may be high when a large number of elements are deployed, which results in a low energy efficiency even if there is an increase in the spectral efficiency. On the other hand, an increase in the number of elements does not necessarily increase the performance since it significantly increases the delivery overhead and may compress the data transmission time. Thus, number configuration for the RIS-aided systems appears to be particularly important. To demonstrate the advantages of the number configuration for the RIS-aided systems, in this article, we first introduce the overview of the RIS that is profitable for number configuration. Then, several design frameworks of number configurations are proposed and discussed, such as hybrid RIS, element on-off control, hybrid phase shift, phase delivery, etc., followed by numerical results to demonstrate the achieved benefits of dynamic number configuration. Furthermore, future directions for number configuration applications are presented, including functionality extensions and framework extensions. Finally, the open issues of realizing number configuration for the RIS-aided systems are outlined and elaborated upon.

I. Introduction

Recently, wireless communication networks have undergone rapid technological advancements and widespread application proliferation, which significantly accelerates the development of emerging applications, such as the Internet of Things (IoT), autonomous driving, and telemedicine [1], [2]. These applications require unprecedented levels of reliability, ultralow latency, and massive connectivity, which exerts immense pressure on existing wireless infrastructures. Among the key issues are the limitations in optimizing the spectral and energy efficiency using traditional network architectures, which are often constrained by factors, such as hardware complexity, signal processing overhead, and propagation environments. To address these challenges, researchers have explored innovative solutions to reimagine wireless system design and spectrum efficiency improvement strategies.