## 4 Results and Discussion
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Authors** & **Methods** & **Contributions** \\ \hline [PERSON], [PERSON], [PERSON] \& Poor [64] & Iteration power allocation algorithm and PREM algorithms & The simulation results demonstrate that their suggested algorithms may approach the exhaustive search strategy and outperform the current optimum NOMA methods. In addition, they exploit the impacts of the number of moving end VNs, which may provide directions for constructing the future vehicular network. \\ \hline [PERSON] et al. [96] & Deep reinforcement learning algorithm named ACDQN & The suggested strategy is demonstrated to achieve its optimal value with a computational overhead of approximately 10\%, 27\% and 69\% lower than existing methods. \\ \hline [PERSON], [PERSON], and [PERSON] [99] & Deep reinforcement learning (DRL) & In addition, by attaining the shuttered-form approach to the matter of resource apportionment issue, we ideally reduced the offloading energy consumption. The suggested procedure was completed rapidly based on modelling findings, and the NOMA-MEC strategy performed better than the prevailing OMA schemes. \\ \hline [PERSON] et al. [102] & Signal-to-interference noise-ratio & It is shown by numerical findings that the NOMA scheme outperforms the OMA system. \\ \hline [PERSON] et al. [103] & Soft Actor-Critic (SAC) algorithm, MASAC-based approach, and MADDPG framework & Results from numerical simulations demonstrate that our suggested strategy is superior to benchmark approaches. \\ \hline [PERSON] et al. [104] & Intelligent massive RAN (mRAN) architecture & Prioritizing network intelligence, the authors provided a comprehensive evaluation of the capabilities of AI and ML techniques in network administration, assigning resources, bandwidth distribution, peripheral communication, and safety. In addition, they identified significant research difficulties and obstacles for each facet of 6G mRANs. \\ \hline [PERSON] et al. [107] & Effective communication deep neural Network & Simulation findings indicate that the suggested CDNN framework is a viable choice for improving the effectiveness of MIMO-NOMA in relation to power distribution, and thorough simulations demonstrate that it accomplishes an advanced cumulative data proportion and better energy proficiency than current solutions. \\ \hline [PERSON] et al. [111] & Deep reinforcement learning (DRL), deep deterministic policy gradient (DDPG)-based algorithm, named HAMEC & The investigational findings indicate that HAMEC performed better than benchmark systems. \\ \hline [PERSON] et al. [113] & An improved multiobjective cuckoo search (IMOCS) algorithm & Simulation findings indicate that the IMOCS algorithm beats four benchmark algorithms, providing the ideal workload scheduling strategy for MEC servers in 6G networks. \\ \hline [PERSON] et al. [114] & RIS-assisted downlink in a NOMA network with p-CSI across Nakagamim fading channels without direct connection situations. & Based on experiment results without a direct link from the base, it has been observed that NOMA aided by RIS exhibits a low outage probability compared to traditional NOMA. Closed-form formulas calculated using Monte Carlo simulations indicate that the coverage probability of a remote user is higher than that of a local user. \\ \hline [PERSON] et al. [115] & Bi-directional long short-term memory (Bi-LSTM) & In the simulated results, the efficacy of the suggested model is evaluated to that of the convolutional neural network model and conventional CE schemes like MMSE and LS. It is demonstrated that the recommended technology offers attainable performance enhancements in terms of symbol-error rate and signal-to-noise ratio, making it appropriate for 5G wireless communication and beyond. \\ \hline [PERSON] et al. [116] & Genetic algorithm (GA), SC assignment and artificial bee colony (ABC) algorithm & The implementation results indicate that the recommended power distribution procedure can swiftly unify to the most effective approach and that the MOS can be increased by boosting the range of customers and scheduling centres (SCs). \\ \hline \hline \end{tabular}
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