Joint Beamforming Optimization Design and Performance Evaluation of RIS-Aided Wireless Networks: A Comprehensive State-of-the-Art Review

Abstract

Reconfigurable intelligent surface (RIS) has emerged as a promising technology for supporting sixth-generation (6G) wireless communication systems. RIS intelligently manages signal reflection properties to enhance signal strength and quality at the receiver. RIS devices are inherently passive, requiring minimal power to perform signal manipulation. Through signal path optimization, interference and transmit power reduction, RIS advances energy efficiency. This aligns with the eco-friendly goals of 6G. However, efficient optimization in RIS-aided networks plays a crucial role in fully unlocking the potential of RIS. Additionally, performance evaluation of RIS-aided systems contributes to understanding optimal configurations and design guidelines for real-world RIS deployments. Despite the contributions from performance evaluation and optimization, RIS confronts various design constraints that demand innovative approaches to optimization and evaluation of their performances. Implementing RIS poses challenges in optimizing configurations for dynamic environments, handling computational complexity in large-scale systems, and adapting to changing wireless conditions. This work provides a comprehensive and in-depth survey of the state-of-the-art in joint optimization designs and performance evaluation for RIS-aided wireless systems. Furthermore, we identify areas that require further investigation to facilitate future research in addressing the existing gaps in knowledge. Finally, the survey discusses promising future research directions that include exploring optimization techniques ensuring global optima with computational efficiency, investigating analytical approaches for accurate network performance assessment under diverse conditions, advancing multiobjective joint optimization, developing application-specific integrated performance metrics, examining the joint impact of relevant RIS design parameters, investigating adaptive quantization schemes, and exploring reliable channel state estimation techniques that are resilient against dynamic environments, noise, interference, and delays.