# Cooperative Power-Domain NOMA Systems: An Overview
## 3 Types of NOMA
### Power Domain NOMA
#### 3.2.5 MIMO-NOMA with Beamforming  
Multi-cast beamforming is yet another method used in MIMO-NOMA to better systems' total capacity, particularly in the event of many users. Specifically, there are single-beam and multi-beam methods. All users in a group receive the same beam in the single-beam method, but in multiple-beams, different beams are provided to different user groups [119].  
NOMA-beamformingThe authors in [120] investigated the beamforming approach in a DL scenario for a MIMO-NOMA with a multiple-user system. In this method, a user pair with differing channel characteristics share the same beam. To decrease interference, the authors suggested a user clustering and power distribution method. This approach maximised the system's total capacity. In this technique, a fairly minor signal was sent to the weaker users (because of bad channel characteristics), while both lower and higher priority signals were sent to the strong users closet to the BS.  
Random beamforming NOMAOn the BS side, the authors of [121] addressed the application of the random beamforming approach. In this method, each user in a cluster receives a single beam with the same coefficient as the power allocation. The authors further suggested a spatial filter to minimise inter-beam and inter-cluster interference. Furthermore, the authors used the idea of reuse of fractional frequency to enhance power distribution across many beams.  
Zero-forcing beamforming (ZFBF)This method reduces inter-cluster interference in varied channel conditions. To reduce interference and achieve maximum performance, the authors of [122] presented a dynamic power allocation and user clustering method. Moreover, these authors used the ZFBF approach in a downlink scenario for a multi-user MIMO-NOMA system.  
Robust beamforming NOMAIt is notable that a majority of researchers discussed beamforming design in NOMA systems with the perfect CSI knowledge. However, given the quantization and estimation issues of the channel, it is difficult for the BS to determine the users' desired CSI. In [123], a resilient beamforming approach based on a framework for worst-case optimization was investigated to solve the problem of norm-bound channel uncertainty in downlink multi-user-MISO-NOMA systems. For users within a cluster, a single beamformer was utilised in this method.  
Coordinated beamforming (CBF) NOMA:The authors of [124] examined the issue of interference in MIMO-NOMA system. The interference alignment CBF (IA-CBF) and interference channel alignment CBF (ICA-CBF) algorithms were suggested by the authors. Two BSs cooperate with each other with respect to their beamforming vectors in these systems to improve the QoS of cell-edge users without exchanging data across cells. Inter-cluster and inter-cell interference are eliminated in both of these methods, while intra-cluster interference is mitigated by NOMA's SIC approach.  
NOMA-maximum ratio transmission (MRT):The authors of [125] looked at the development of information broadcasting that switches to MMSE beamforming and NOMA-MRT to keep the sum rate constant. The goal of this technique was to obtain the highest possible sum rate of the system. The authors also claimed that, with its correlated and fully aligned channel vectors, MMSE beamforming achieves a rate that is almost comparable to NOMA-MRT. With an orthogonal channel, however, the MMSE beamforming outperforms the NOMA-MRT.