## 2 Research Background
### Optimization Methods Developed for RIS Analysis
It is discovered that increasing the number of RIS reflecting elements is more helpful than increasing the number of antenna elements at the BS. The authors in [41] recently addressed the problem of a more robust eavesdropping channel and improved the legitimate user's secrecy rate by constructing the AP's transmit beamformer and RIS phases together. The authors in [42; 43] examined the maximization of the SINR for the multi-user MISO scheme. Furthermore, correlated Rayleigh channels are considered between RIS elements and users, and an algorithm for optimizing the phases of the RIS is suggested. The authors in [44] provided more extensive simulation results on energy efficiency and system sum-rate for a more practical system design and system characteristics. The transmit beamforming with this approach needs complete channel state information (CSI) and knowledge of RIS phase terms at the BS. In [51], the authors contributed to the most recent research (HyperSurface design). They proposed a sensing system for estimating radio waves impinging on HyperSurfaces that can work without external or internal hardware, such as field nano-sensors. It is demonstrated that hypersurfaces may be programmed to detect and manipulate waves at the same time. In [52], authors suggested an approach based on machine learning (neural networks) to adaptively configure HyperSurfaces-assisted programmable environments. \begin{table}
\begin{tabular}{p{42.7 pt} p{341.3 pt}} \hline \hline
**References** & **Existing RIS-Assisted Schemes** \\ \hline
[45; 46; 47] & For multi-user downlink transmission, RIS scheme with periodic-aided structure is considered. Maximization of energy efficiency and sum rate is performed with finite resolution reflectors. \\ \hline
[42; 43; 48; 49] & For massive MISO multiuser system design, the joint active and passive beamforming problem is discussed. Using square law scaling in transmitted power and optimization, minimization of base station transmit power is performed. \\ \hline
[4, 35; 50] & For the estimation of the symbol error probability (SEP) of RIS supported systems, a mathematical method is proposed. Further, by considering the RIS as an AP, the concept of RIS (LIS)-assisted IM is developed. For the LIS-SM and LIS-SSK systems, greedy and maximum likelihood detectors are formulated, along with theoretical derivations. \\ \hline
[36] & The capacity is increased by RIS phase optimization, and for massive MIMO is with Rician channel is considered. \\ \hline
[44] & The problem of RIS phase shifter and optimal transmit beamforming is analyzed in order to increase achievable spectral efficiency. \\ \hline
[37] & For an RIS assisted MISO system, a new channel estimation protocol with energy harvesting feature is proposed. \\ \hline
[40] & An RIS assisted secure communication system with legitimate receivers and eavesdropper is concerned. \\ \hline
[39] & Joint optimization using RIS reflecting coefficient and BS beamformer for MISO system. \\ \hline
[51; 52] & A sensing system for estimating radio waves impinging on HyperSurfaces is proposed. It is demonstrated that HyperSurfaces may be programmed to detect and manipulate waves at the same time. \\ \hline
[53; 52] & Further, an approach is suggested based on machine learning for HyperSurfaces-assisted programmable environments in order to configure it adaptively. \\ \hline
[5, 53] & The maximization of the minimum SINR is analyzed for RIS -MIMO System. Further, for phase optimization, LOS channels, large scale fading statistics and correlated RIS channels are considered. \\ \hline
[4] & By considering RIS as AP, RIS assisted IM is along with mathematical optimization (such as Greedy Algorithms). \\ \hline
[38] & RIS architecture with sparse channel sensors (few of the existing RIS active units) and the use of Deep learning techniques are presented. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Recent state of the art for RIS-assisted schemes.