## 4 Integration of DL-Based NOMA with Emerging Technologies  
### Mobile Edge Computing (MEC)  
Tasks may be completed at the edge server due to mobile edge computing (MEC), which is seen as a potential method of offering low-latency computing services. However, because MEC servers often have limited compute resources due to the high deployment costs, collaborative edge computing, which may make use of dispersed MEC servers, provides an effective way to increase system processing capacity [121]. Therefore, it is important to look into the computation offloading problem in collaborative edge computing, which includes the task offloading problem, which determines which server is chosen to complete the task, and the resource scheduling problem, which establishes how many communication and computation resources are allotted to the task. Numerous studies have recently examined the compute offloading issue in NOMA-MEC systems [122; 123]. The majority of current approaches concentrate on the short-term scenario when the task request is predictable, and the environment of the system remains stable [124; 125; 126; 127; 128]. When using this strategy, task offloading, and resource scheduling decisions must be promptly updated whenever the system environment alters. This strategy typically involves significant computational complexity and results in severe performance degradation because it fails to recognize the relationship between various scheduling time slots in a dynamic IIoT environment. To better understand dynamic computation offloading schemes, see [129; 130; 131; 132]. These plans, however, do not take into account the collaboration between several edge nodes, which might lead to load-balancing issues. The work offloading strategy for NOMA-based collaborative edge computing, as investigated in [133; 134], is established based on users' serving distances [135] or signal power [133], which does not inherently address the load balancing issue. Some works suggest a dynamic compute offloading method to fulfil the long-term quality of service (QoS) of delay-sensitive operations to overcome this issue and fully use the advantages of combining NOMA with collaborative edge computing in the IIoT.  
Figure 7: Sum rate versus SNR for the conventional and proposed SIC schemes with varying power allocations [98; 119; 120].  
In [136], the authors provide a DRL-based solution for supporting the binary computing offloading policy, which optimizes the task offloading choice and the sub-channel assignment to maximize task fulfilment. Every agent for an IIoT device uses a recurrent neural network (RNN) to forecast the load status of sub-channels and MEC servers; this information is then utilized to inform a Reinforcement Learning (RL) agent's decision. The suggested prediction-based-DRL (P-DRL) technique is shown to be able to attain a greater task completion ratio than existing systems through simulation. In Table 4, we have summarized studies on the MEC scenario. In a recent work [132], the authors propose a hybrid SIC (H-SIC) scheme, which dynamically adapts the SIC decoding order among all NOMA groups. A DRL-based algorithm was proposed to obtain a close-to-optimal user grouping policy. The authors optimally minimized the offloading energy consumption by obtaining the closed-form solution to the resource allocation problem. Simulation results presented in Figure 8 show that the proposed H-SIC and H-SIC with task assignment outperformed the existing orthogonal multiple access (OMA) schemes. While Figure 9 presents the NOMA-MEC scenario for both uplink and downlink.  
Figure 8: Average energy consumption versus the offloading data length [132].  
Figure 9: NOMA-MEC scenario for uplink and downlink.