## 4 Integration of DL-Based NOMA with Emerging Technologies
### MIMO-Assisted NOMA  
Massive multiple-input multiple-output (MIMO) has been extensively researched as a promising method for enhancing the capacity of a communication network by merely adding more antennas [166; 167]. The use of MIMO gives extra degrees of freedom for further performance optimization. Hence the use of MIMO over NOMA is quite interesting [168]. MIMO-NOMA is a strong option to help communication networks achieve even greater spectral efficiency and lower latency. MIMO-NOMA has generated a lot of attention because of its promising performance, and several MIMO-NOMA-based methods have been put forth. NOMA is included in beamspace MIMO in mmWave transmission, a novel approach with a high spectrum and energy efficiency presented in [169] to increase bandwidth and energy efficiency. Authors [170] explored a multi-cell MIMO-NOMA system in the downlink. They deduced a few interference alignments (IA)-based coordinated beamforming strategies where the beamforming vectors were mutually optimized at two BSs to reduce inter-cell interference and boost the throughputs of the cell-edge users. Furthermore, in [171], the authors investigate the energy efficiency (EE) problem in a millimeter wave massive MIMO system with non-orthogonal multiple access (NOMA). They formulate a power allocation problem aiming to maximize the EE under users' quality of service requirements and per-cluster power constraint. Additionally, a unique MIMO-NOMA transmission approach was developed that outperformed both OMA transmission and the signal alignment NOMA scheme in terms of total power consumption [172]. This was accomplished by creating precoders and equalizers for users within the same group. Fundamentally, MIMO-NOMA systems have been shown to offer a better capacity and data rate than 4 th generation (4G) MIMO-OMA systems. Power allocation algorithms have been studied in a number of ground-breaking studies. The authors in [173] investigated the cumulative data rates for the current power allocation strategies for a downlink MIMO-NOMA system with two users grouped into each cluster. For an indefinite rate pair obtained by MIMO-OMA, it was stated in [173] that a power splitting method was necessary to accomplish the rate pair by MIMO-NOMA with less power consumption. A multilevel security model-based opportunistic NOMA method for 3D MIMO channels was suggested. A range of security levels is assigned to the BS and users to increase the network throughput and secrecy capacity of MIMO-NOMA [174]. Based on the MIMO-NOMA system, the researchers in [175] suggested dynamically grouping the receive antennas at the users into a certain number of clusters and deriving power allocation solutions to increase the overall cell capacity. The number of clusters seems to be at least equal to the number of transmit antennas at the BS. The precision of the channel state information (CSI), which is required by SIC for each user, has a significant impact on the possible gain of MIMO. The existing power distribution plans are predicated on flawless CSI. Furthermore, it is very challenging to obtain the precise CSI in MIMO-NOMA systems owing to their unreasonably large complexity. The effectiveness of the present power allocation techniques suffers as a result. Despite sparsity information being essential for MIMO channel estimation and precoding, there have been few attempts to investigate the spatial degrees