# Cooperative Power-Domain NOMA Systems: An Overview
## 3 Types of NOMA
### Power Domain NOMA
#### 3.2.7 Reconfigurable Intelligent Surface (RIS)-Aided NOMA
According to [141], employing NOMA transmission results in greater average data rates for situations in which users are spread out over a wide range (distance) of access points, demonstrating that NOMA is a convincing approach for THz networks. In the literature, the RIS-assisted THz network is examined [142]. In addition to these investigations, there is need for research into the RIS-assisted NOMA transmission terahertz communication network. _Power minimization in RIS-NOMA:_ Power efficiency and energy efficiency (EE) are crucial measures for assessing the future effectiveness of wireless communication networks. The performance of NOMA and OMA for transmit power minimization was examined in [143], which offers a crucial rule for user pairing in RIS-aided systems with multiple users and resource blocks. In [144], a multi-cluster MISO RIS-NOMA system was investigated with the goal of reducing the overall transmit power. This paper proposes an optimization approach based on second-order cone programming (SOCP), beamforming, and phase shift to reduce the BS's overall transmit power. In contrast to single-RIS-assisted NOMA networks [145], a multi-RIS and multi-cluster NOMA network was examined in [146]. It was suggested that the transmit power at the BS can be minimised by jointly optimising the beamforming at the BS, power distribution to the NOMA users, and RIS phase shifts. _Energy efficiency (EE) maximization in RIS-NOMA:_ Another crucial indicator for future green communication networks is EE. The ratio of information bits provided to energy consumed is known as the EE. The goal of EE maximization is to obtain the best trade-off between a better sum rate and less power, which is distinct from transmit power minimization and sum-rate maximization and creates a fractional programming challenge. The EE maximization issue was investigated in RIS-NOMA networks based on two-users models [147]. Driven by the advantages of RIS-NOMA, beamforming optimizations based on sequential convex approximation (SCA) and phase shift optimization algorithm based on SDR were suggested for maximization of EE. Additionally, by allocating subchannels based on matching theory, difference-of-convex (DC) programming can be used to maximise the EE of NOMA systems [148]. \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**RIS-NOMA Variant** & **Problem Discussed** & **Variables** & **Results** & **Open Issues** \\ \hline \multirow{6}{*}{**Throughput and Data Rate Maximization**} & SIS-RIS-NOMA [149] & Power, phase shift & Weighted sum rate performance & Optimal PACs \\ \cline{2-5}  & & Phase shift, active beamforming & Maximises system sum rate & Additional powertransmission \\ \cline{2-5}  & mmWave aided-RIS-NOMA [136] & Beam selection, active beamforming & Provides a near-optimal solution & Resource allocation scheme \\ \cline{2-5}  & Massive MIMO-RIS-NOMA [138] & Phase shift of RIS & Dual-polarised RISs with limited feedback \\ \hline \multirow{6}{*}{**Power Minimization**} & SISO-RIS-NOMA [143] & Beamforming at BS & User pairing in RIS-NOMA & Incremental redundancy \\ \cline{2-5}  & MISO-RIS-NOMA [150] & Phase shift of RISs, beamforming & Minimises transmit power & Multiple antennas at receiver \\ \cline{2-5}  & MISO-RIS-NOMA [147] & Phase shift of RISs, beamforming & Maximises system energy efficiency & Limited CSI knowledge \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Selected significant RIS-NOMA systems discussed in literature.