The demand for enhanced wireless communication capabilities, driven by visions for 6G and the evolution of 5G networks, necessitates innovative approaches to overcome the limitations of existing infrastructure [1, 4]. Traditional methods, such as scaling up MIMO systems, face significant challenges related to hardware complexity and power consumption [6, 7]. Reconfigurable Intelligent Surfaces (RIS), also known as Intelligent Reflecting Surfaces (IRS), have emerged as a promising paradigm to reshape the wireless environment passively and smartly [2, 3, 16, 25]. An RIS is a planar surface composed of numerous passive reflecting elements, each capable of independently altering the phase and/or amplitude of an incident electromagnetic wave [2, 3, 16]. By controlling these elements, the RIS can collectively steer reflected signals towards desired receivers, effectively creating favorable propagation conditions and mitigating detrimental effects like fading and interference [2, 3, 18, 19]. This article reviews key approaches for enhancing wireless communication performance using RIS and discusses methodologies for evaluating these performance gains. We explore the fundamental principles of RIS-aided communication, various techniques for joint optimization of active and passive beamforming, power control, and diversity [14, 18, 19, 20, 21, 22, 23, 24]. Furthermore, we examine the crucial role of performance evaluation metrics and simulation/analytical techniques in quantifying the benefits of RIS-assisted systems [14, 27, 28, 29]. Drawing upon recent literature [1-29], this review highlights the potential of RIS to significantly improve metrics such as signal-to-noise ratio (SNR), coverage, energy efficiency, and diversity, paving the way for more robust and efficient future wireless networks.

Reconfigurable Intelligent Surface (RIS), Wireless Communication, Performance Enhancement, Wireless Network Optimization, RIS-Aided Schemes, Communication System Evaluation

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Karimullah S, Vishnuvardhan D (2023) Pin density technique for congestion estimation and reduction of optimized design during placement and routing. Applied Nanoscience 13: 1819-1828.