## 4 Results and Discussion
### Benefits of NOMA for Edge Computing  
NOMA and MEC are potential technologies for the next generation of wireless networks. With the rapid development of intelligent communication, mobile edge computing is increasingly applied to future contact, can increase the computing power of an application user, and can play a crucial role in virtual reality, augmented reality, unmanned driving, and similar domains. Unlike typical cloud computing systems, mobile edge computing may supply computational power in a cloud radio access network. For example, with a mobile edge computing server integrated with a base station, a user may offload computing chores to the mobile edge computing server placed at the network's edge, which executes task computing. As the mobile edge computing server may be close to the user, a mobile edge computing network can provide the user with task computing services with low latency and energy usage. For moving edge computing, there are two modes of operation: the partial offload model and the binary offload model. In the partial offload approach, computational activities may be divided into two halves, one for local execution and the other for offloading to the mobile edge computing server. Under the binary offload approach, computational operations may be locally or completely offloaded to the mobile edge computing server. To address the ever-increasing data traffic, NOMA has been recognized as a vital technology for the upcoming generation of mobile communication networks. By employing superposition coding at the transmitter and successive interference cancellation at the receiver, NOMA can achieve higher spectral efficiency than traditional OMA techniques. If NOMA uplink, downlink transmission and mobile edge computing are integrated, latency and energy consumption may be greatly lowered by using NOMA mobile edge computation offload, a major improvement for enhancing the mobile user experience. Nonetheless, no technique or technological solution is presently developed in the prior art. The technical configuration is as follows: To accomplish the objective mentioned above, the present invention employs the following technological solutions: The next stages constitute a technique for developing a NOMA-based mobile edge computing system:  
1. establishing a system model wherein the system comprises two users and a base station equipped with a MEC server; the users and the base station are both fitted with a single antenna, and information transmission is carried out on the uplink in a NOMA mode, a wireless channel adopts a frequency non-selective quasi-static block fading model, the channel state is maintained unchanged in a selected given transmission block period, and the duration is limited, and 2. transmitting information over the uplin \begin{table}
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**Authors** & **Methods** & **Contributions** \\ \hline [PERSON] et al. [118] & Networking systems of AI (NSAI) & This paper’s contribution entails (1) offering an all-encompassing structure for the deep convergence of computing and communications, where the network and implementation can be collectively improved as one cohesive unit, and (2) suggesting the overall strategy and addressing open investigations in acknowledging the online-evolutionary convergence of the digital world, the natural environment, and society as a whole, towards the ubiquitous neurological systems (UBNs), which require cooperation in the functioning of the internet, the physical world, and society at large. \\ \hline [PERSON] et al. [119] & Hybrid cuckoo search (CS) and genetic algorithm (GA) & The hybrid approach presented boosts throughput by 17\% and 8\%, respectively, as likened to the cuckoo search and genetic algorithms employed separately. The efficiency of the suggested technique is encouraging for scenarios in which the handoff techniques must be tuned to regulate repeated handoffs to lower the power consumption of user equipment further. \\ \hline \hline \end{tabular}
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Table 6: _Cont._2.