## 2 Research Background
### Optimization Methods Developed for RIS Analysis  
The authors in the study [34] proposed a discrete (finite resolution) RIS process. In addition, the author presented a mathematical method for calculating the average SEP of RIS-assisted systems in [35]. The idea of using a RIS as an AP (transmitter) by using an unmodulated carrier for intelligent reflection is also being investigated. Using computer simulations, it was proved that doubling the number of reflecting elements reduces the SNR by 6 dB while maintaining the same SEP. To put it another way, the average received SNR of RIS-based systems is proportional to the square of the total number of reflecting elements (N). Further, in [4], the authors proposed the large, intelligent surfaces-spatial modulation (LIS-SM) and LIS-space shift keying (SSK) schemes to understand the concept of intelligent surfaces-assisted index modulation (IM) techniques. The phase optimization of intelligent surfaces and the principle of index modulation are combined in these schemes to improve spectral performance and signal quality. The theoretical average bit error probability is calculated using a unified framework that includes maximum energy-based suboptimal (greedy) and exhaustive search-based optimum (maximum likelihood) detectors. Computer simulations reveal that the resulting bit error probability is much higher than traditional fully digital precoding-based receive SSK methods. In [36], the authors considered a RIS-assisted large-scale antenna system in which a BS communicates with a single antenna user. Precoding can be implemented, and the system's ergodic capacity is enhanced by optimizing the RIS phases, as in previous works, assuming knowledge of CSI and RIS phases at the BS. The authors also investigated different Rician K factors and discontinuous phase shifts. Computer simulations demonstrate that using 2-bit phase shifts can result in a high capacity. In rank-one or full-rank LOS channels between the BS and the RIS, the authors in [5] evaluated the optimization of the minimal SINR for a RIS-assisted multi-user MISO system. Furthermore, correlated Rayleigh channels are studied between RIS elements and users, and an algorithm for optimizing RIS phases in the presence of large-scale fading is provided. The authors of [37] suggested a new channel estimation protocol for a single-user MISO system with energy harvesting that uses RIS. The authors take into account the fact that RISs do not have active components in particular. Both active and passive near-optimal beamforming designs are designed to enable effective power transfer. The authors in [38] proposed a deep learning-based method. The RIS learns how to deal with incoming signals in the most efficient way possible by utilizing the predicted channels at active elements. Two independent strategies for RIS design in the presence of unknown channel information, one based on compressive sensing and the other based on deep learning, to lower the training overhead in the presence of passive RIS elements. A novel RIS design based on sparse channel sensors is proposed, with part of the RIS units being active (connected to a baseband processor). In other words, the RIS uses many active aspects to make the channel estimation process easier. In [39,40], the physical layer security of RIS-based systems was considered for a downlink MISO broadcast system with numerous legitimate listeners and eavesdroppers. The authors developed a minimum secrecy-rate maximization problem by jointly optimizing the beamformer at the BS and reflecting coefficients (continuous and discrete) at the RIS. The authors present both globally optimum and low-complexity sub-optimal algorithms. The authors in [39] looked closely at a RIS-assisted secure communication system with a legitimate recipient and an eavesdropper. The secrecy rate is improved by optimizing the beamformer at the BS and the phase shifts at the RIS. In addition, the secrecy rates of massive MIMO and RIS-assisted systems are compared.