## 2 Related Work  
Cell outage compensation in NOMA-aided networking has been disregarded despite all the efforts, and it has to be researched. In [5], the authors take into account the issue of a joint allocation scheme and user interaction. Traditional optimization-based numerical algorithms can be used to tackle the problem of power allocation in a NOMA-based system. In [20], power distribution in the uplink and downlink were modeled as convex optimization issues, with closed-form methods provided. In [21], an imperfect NOMA scheme with interference residue and receiver sensitivity from non-ideal decoding was investigated. An iterative approach was developed to address the resource allocation optimization issue, which considered a trade-off between proportional fairness and maximum throughput. These optimization-oriented strategies, however, must be repeated anytime network parameters alter over time. As a result, they suffer significant online computing costs and substantial overhead, impeding real-time development. To address the power allocation problem in NOMA systems, meta-heuristic techniques, including particle swarm optimization [22] and genetic algorithms [23], have been applied. However, they have the same limitations as the previously stated optimization-aided numerical techniques. Learning-based techniques have recently been developed in light of these constraints. RL is a prominent strategy for dealing with the problem of uncertain and variable environments [24]. RL-based strategies for power allocation in an outage compensation situation were put forth in [25; 26]. However, one disadvantage of RL-based techniques is that they may take a very long time to converge as they attempt to identify the best answer  
Figure 2: An illustration of DL-based NOMA-VLC system. through the interaction of their environment via trial and error. Because of this, they are less desirable in real-world cell outage compensation conditions when the network must respond quickly to a catastrophic loss and keep customers connected. On the other hand, unsupervised learning methods do not make use of the data that can be obtained when a network is operating offline. In [5], the authors suggest a supervised deep neural network (DNN) to compensate for the cell outage compensation issue by using the data that may be acquired offline and attain substantially reduced online computing complexity. For a variety of resource allocation issues in wireless networks, including beamforming [27], subchannel assignment [28], power control [29; 30], and user association [31; 32], it has been demonstrated that DNNs are a potential method for approximating the best policy. While DNN-aided resource allocation in wireless connections has been the subject of several studies, none have looked at how it may be used to address the issue of cell outage compensation, which is the subject of new research works. The review papers [33; 34; 35] offer a thorough analysis of DL-aided interaction. The evolution and significance of DL are examined. The authors discuss how DL compares to potential wireless technologies. They also outline the potentials, difficulties, and future research directions for DL in a wireless environment. They show how the DL-aided NOMA system has improved spectrum efficiency, channel state estimation and system capacity. The discussion of massive MIMO's direction of arrival (DoA) and channel estimation applies to DL-assisted MIMO systems. They provide an analysis of DL performance in mmWave communication under conditions of extremely high-power consumption and constrained link gains. [36] discusses NOMA's function in the communication system. The full advantages of NOMA and NOMA in combination with several technologies, including mobile edge computing (MEC), visible light communication, energy harvesting, physical layer security (PLS), cognitive radio (CR), mmWave communication, cooperative communication, and massive MIMO are provided. Additionally, it exemplifies how studies on NOMA's cohabitation with other systems are headed.