### Background  
Demanding applications such as virtual reality (VR), internet gaming, and high-definition (HD) films have contributed to a burgeoning information explosion [12] during the past decade. The advent of 5G has posed new difficulties in the areas of extensive connection, energy efficiency, peak data throughput, reduced latency, ultra-reliability, and spectral efficiency [13]. The rapid expansion of the Internet of Things (IoT)-based massively heterogeneous networks has necessitated incorporating a number of notable difficulties into 5G technology. Major obstacles to adopting modern multiple access systems now exist due to the uplink or downlink transfer of substantial user data between different networks. NOMA, an intriguing and potential solution for 5G networks, has attracted enormous attention recently [14] as a result of the aforementioned difficulties. Industry and academics alike have recognized NOMA as a promising trend and technology for meeting the varied requirements of 5G. Next-generation mobile or wireless networks rely on this  
Figure 1: Deep neural network general framework. crucial enabling technology to meet the varied demands of users for redundancy, speed, fairness, throughput, and connection. Through signal superposition, NOMA can serve an arbitrary number of users in each resource block. Additionally, they may allocate power resources to nodes with poor channel characteristics to improve throughput. Compared to traditional multiple access (MA) methods, NOMA makes better use of available resources [15; 16]. Unlike NOMA, each orthogonal resource unit in conventional MA techniques serves a single user. This has an adverse effect on the system's overall throughput and spectrum effectiveness. When NOMA is used in these circumstances, it assures that not only users with poor channel characteristics are supplied, but also users with better channel conditions can consume a similar amount of bandwidth as the weak user. The multiuser signals in NOMA are broadcast to the end users after being multiplied in the transmission part through superposition coding (SC) at various power levels based on nonorthogonal symmetry. Compared to consumers with better channel conditions, those with poorer channel conditions often receive more power. Therefore, having the proper channel state information (CSI) to transmit data has become more important in NOMA. With a strong channel gain, users may readily retrieve the signals gathered in the receiver. Users that experience weak channel capability mistake other signals for interference, which significantly lowers spectral efficiency. With NOMA, in the absence of a guard period and no signal interference, this issue may be solved. NOMA improves user fairness and provides great performance [17]. In contrast to fourth-generation (4G) networks, NOMA in 5G systems is primarily employed to enable larger user density and achieve high spectrum efficiency and low latency [18]. Significantly higher data speeds, increased system capacity, enormous numbers of mobile device connections, decreased latency and decreased power consumption may all be supported by 5G. A wide variety of data transmission entities with various data rates and latency demands make up 5G-enabled systems, including IoT systems. In 5G networks, where several users depend on the same resources, NOMA is employed. In the context of high spectral efficiency and dependable connection among multiple data transmission entities, NOMA in 5G systems is anticipated to satisfy the desiderata of 5G communication systems. Howbeit, NOMA systems have specific drawbacks, including high computational complexity, difficult designs, and issues with resource allocation. Furthermore, to perform consecutive interference nullification at the receiver side, NOMA systems also need flawless CSI. Perfect consecutive interference nullification is critical for improving NOMA performance. Without understanding the correct CSI at the system's transmitter, it is extremely complex to build a successful power allocation (PA) technique.