# NOMA-Based VLC Systems: A Comprehensive Review
## 3 Integration of NOMA-Based VLC with Emerging Technologies
### Machine Learning Techniques for NOMA-VLC  
With the introduction of machine learning (ML) algorithms, particularly deep learning (DL) and neural networks (NNs), VLC has the potential to become more efficient and solve numerous issues at the physical layer [127]. Multi-CAP and Nyquist pulse amplitude modulation (PAM) VLC systems were addressed in [128; 129], respectively, with a clustering algorithm-aided perceptual choice approach and a nonlinear compensation mechanism. In order to compensate for PAM-transmission VLC's flaws, a post-equalizer based on deep long-short-term memory (LSTM) was developed. The use of red, greenish, and blue LEDs in deep learning (DL)-aided multi-colored VLC connection was described in [130]. With the use of machine learning, the authors of [131] explored the possibility of using this technique to build and implement a VLC connection for demodulating signals.  
To address the linear and nonlinear deformities that plague NOMA-VLC transmissions, the authors in [127] present a convolutional NN (CNN)-based signal demodulator as presented in Figure 10. Recorded NOMA signals are utilized for: (i) offline CNN training and (ii) online signal compensation and recovery using a CNN-based demodulator. Re  
Figure 8: Block diagram of two-user NOMA-based VLC system with QAM and O-OFDM.  
Figure 9: BER performance analysis for NOMA-DCO-OFDM (**a**) User1 PA 0.9 and User2 PA 0.1 (**b**) User1 PA 0.6 and User2 PA 0.4.  
member that the NOMA signal can be detected without the free-space channel response. They demonstrate, via modeling and experimental data, that the recommended CNN-based demodulator would successfully attenuate nonlinear and linear distortions, therefore enhancing the system's functionality.  
To increase the spectral efficiency of a MIMO-NOMA system when facing interference from a smart jammer, the researchers in [132] suggested a swift RL-based power allocation technique. For mobile edge computing with NOMA, the research in [133] employed Q-learning to create a framework. DRL solves a problem with Q-learning related to Q-table storage and lookup by combining deep learning into RL. Power allocation in cache-assisted NOMA systems was created by [134] authors using dynamic resource balancing. A DRL was employed in [135] to find sub-optimal power allocation strategies for an uplink multicarrier NOMA system. In conclusion, a combined channel assignment and power allocation problem in a two-user NOMA system was addressed by [PERSON] et al. [136] utilizing a DRL framework. The key goal [137] is to find the configuration between optimal power distribution and optimal LED transmission angle adjustment for a virtual local area network (VLAN) with users spread out evenly around the space. The issue is NP-hard, meaning it cannot be solved by traditional optimization techniques in this context. DQL's greatest strength is in its ability to address difficult joint optimization issues in wireless communication, issues that are typically intractable with the use of more traditional mathematical analysis [138]. Several recent publications have shown that DQL is effective. For example, the researchers in [139] used DQL to forecast and modify the IRS phase shift matrices optimally, allowing for the optimization of an IRS-NOMA system. Although it is impossible to get complete information about the channel state, the DQL method enables the agent to acquire new knowledge about the nature of communication, which can then be used to find the best possible solution. In [123], the authors proposed an energy efficient HO algorithm for UAV-aided VLC-NOMA. As a swarm intelligence tool, the Harris hawks optimizer (HHO) is among the most contemporary algorithms that have gained popularity since its inception. It formulated a joint problem by considering UAV's placement and PA to maximize the sum rate of all users.