## 1 Introduction
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**SI. No.** & **Paper Title** & **Fros** & **Cons** & **Discussion** \\ \hline
1. & Secrecy Rate Maximization for IRS-Assisted Multi-Antenna Communications (2019) [21] & - & Significant secrecy rate improvement by optimizing active transmit and passive reflect beamforming. & Complexity in joint beamforming design. & This paper demonstrates that IRS can significantly boost secrecy rates by careful phase shift optimization, addressing passive reflect beamforming. & This paper demonstrates that IRS can significantly boost secrecy rates by careful phase shift optimization, addressing security in wireless networks. \\  & & - & Enhanced power efficiency and interference suppression & multi-reflection scenarios & This study highlights the potential of IRS in secure communication by optimizing reflect beamforming to enhance desired signals and suppress eavesdropping. \\ \hline
2 & Secure Wireless Communication via IRS (2019) [22] & - & Increased secrecy rates with more IRS elements. & - High correlation between channels can limit improvements. & This paper discusses the practical applications of IRS, underlining its feasibility and effectiveness in enhancing wireless communication systems in controlled environments. \\ \hline
3. & Configuring an IRS for Wireless Communications (2021) [23] & - & Demonstrated practical & - & Potential high implementation cost. & This paper discusses the practical applications of IRS, underlining its feasibility and effectiveness in enhancing wireless communication systems in controlled environments. \\  & & - & - & - & This paper proposes a centralized algorithm using semidefinite relaxation (SDK) for the joint optimization. & This paper proposes a centralized algorithm using semidefinite relaxation (SDK) for the joint optimization of transmit and reflect beamforming, and a distributed algorithm to reduce overheads. The IRS significantly improves link quality and coverage. \\ \hline
5. & Channel Estimation for Reconfigurable Intelligent Surface Assisted High-Mobility Wireless Systems (2022) [15] & - & Effective in high-mobility scenarios. & Complex algorithm implementation. & This research addresses the challenges of channel estimation in high-mobility environments, proposing a novel algorithm that enhances estimation accuracy by keying the RIS’s ability to adjust phase shifts dynamically. \\ \hline
6. & Stacked Intelligent Measure-Aided MIMO Transceiver Design (2024) [14] & - & Ultrafast computational speed due to wavebased processing. & - & Initial exploration phase with numerous open challenges. & This recent work proposes a novel SIM concept for MIMO systems, offering significant improvements in processing speed and energy efficiency but requiring further research to address existing challenges. \\  & & - & Simplified hardware with reduced energy consumption & - & Need for efficient inter-layer transmission models and CSI acquisition methods. & This recent work proposes a novel SIM concept for MIMO systems, offering significant improvements in processing speed and energy efficiency but requiring further research to address existing challenges. \\ \hline \end{tabular}
\end{table} 
Table 1: Pros and cons of state-of-the-art research on IRS-aided wireless communication systems. The problem is split into two sub-problems: channel estimation and IRS configuration. Optimizing the IRS configuration to obtain a better data rate is a non-convex problem. From prior research, it is observed that computationally complex algorithms along the lines of Machine learning [25], Lagrangian Dual Transform [26], and semidefinite relaxation [27] are used to tackle this problem. Whereas in practice, the time taken for optimizing the IRS configuration is as important as optimizing it.