# Performance Analysis of a Cognitive RIS-NOMA in Wireless Sensor Network
### Related Work  
This section provides a brief summary of the combination of the RIS in a NOMA [10; 27; 28; 29; 30; 31; 32; 33; 34] or CR network [35; 36; 37; 38; 39; 40; 41] or RIS-NOMA-CR [42; 43]. By making use of the fixed reflecting components in [10], the system total rate of IRS-NOMA was increased for multi-antenna situations. In [27], the authors developed a distributed and iterative approach for optimizing the phase shift matrix and power allocation in the RIS-NOMA-assisted simultaneous wireless information and power transfer (SWIPT) network. In [28], the developers used active/passive RIS to aid in uplink communication, allowing the adoption of NOMA schemes amongst users with similar transmission power. In [29], a combination of user clustering and RIS allocation techniques was developed to secure the NOMA scheme's execution while optimizing the system's sum rate. The performance of the RIS-powered NOMA network was studied in [30], which looked at energy efficiency (EE) in both delay-restricted and delay-tolerant modes. The authors in [31] developed the IRS-NOMA system throughput maximization by considering the reflection coefficients and the decoding order. In order to get over the hardware constraints, a straightforward IRS-NOMA transmission scheme was created in [32], where the outage probability for a single user is calculated using on-off control. In [33], the authors derived the closed-form formulas of the RIS-NOMA outage probability under Nakagami-\(m\) fading channels based on the passive beamforming weights. In order to improve the coverage of both uplink and downlink NOMA networks, the RIS was implemented [34], demonstrating the advantages of RIS over FD relaying.  
In multiple-input multiple-output (MIMO) CR systems, the authors in [35] used a RIS to help the SUs transmit data. To increase the maximum SU rate that could be achieved, the authors of [36] incorporated multiple RISs to downlink multiple-input single-output CR systems. A simultaneous transmit precoding and reflect beamforming design for RIS-assisted downlink MIMO CR networks was created in [37]. The authors of [38] investigated resilient beamforming design based on both bounded and statistical channel state information (CSI) error models for PU-related channels in RIS-aided CR systems. In order to maximize the secrecy rate for safety purposes, a novel numerical solution has been suggested of a RIS-assisted spectrum sharing underlay CR wiretap channel [39]. In [40], the authors jointly designed the passive beamforming at the RIS and the transmit beamforming at the multi-antenna cognitive base station, resulting in a secrecy EE maximization technique. The authors of [41] focused their attention on potential improvements to the resource allocation architecture for RIS-assisted full-duplex CR systems. In [42], the performance of the RIS-assisted CR-NOMA system was examined, and a deep learning (DL) framework for ergodic capacity prediction was created. The authors in [43] studied the RIS-assisted non-terrestrial under CR and NOMA. Furthermore, the outage performance of secondary users is derived under perfect/imperfect SIC and interference from PN.