### Recent Studies and Insights
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Technology** & **Ref.** & **Source (Journal)** & **Year** & **Contribution** \\ \hline AI & [31] & MDPI Electronics & 2023 & The aim of this study is to enhance 6G native AI training latency and task accuracy by enhancing the quality of AI services (QoAIS) in 6G native AI wireless networks. To achieve this, the study utilize G-TSRA and NSG-TSRA heuristic algorithms. This improved accuracy and reduction in latency in 6G native AI wireless networks by resolving the issue of optimizing task scheduling and resource allocation in AI training services. \\ \hline \multirow{4}{*}{[32]} & \multirow{4}{*}{IEEE} & \multirow{4}{*}{2023} & The objective of this work is to improve the flexibility in resource management and adaptability to the changing service requirements in the 5G/6G networks. Integration of Software Defined Networking (SDN) and cloud virtualization approaches in the context of Multi-access Edge Computing (MEC) architecture is used to achieve this. Thus, allowing rapid adaptation to critical user requirements and ensure service continuity for the expected user mobility in MEC environments enables service delivery and low latency in time critical services. \\ \hline \multirow{4}{*}{[33]} & \multirow{4}{*}{CMES} & \multirow{4}{*}{2024} & The objective of this study is to optimize the performance of FL in 5G/6G networks by considering issues related to non-IID data, low client engagement and heterogeneous device clients. This is achieved by applying the adaptive server selection FedAdaSS algorithm, which utilize cloud computing resources. The study effectively reduces communication overhead by dynamically selecting the best server for each round of training, and random reshuffling on client sides to mitigate performance loss due to low participation in FL processes. \\ \hline \multirow{4}{*}{Cloud} & \multirow{4}{*}{Future Generation Computer Systems} & This study aims at reducing service request delays and achieving low cost in 6G cloud-edge systems. To achieve this, the Service Deployment with Service Rise and Fall (SD-SRF) algorithm is developed for offline service arrangement based on a greedy approach, and the Proximal Policy Optimization for Multi-layer Service Deployment (PPO-MSD) algorithm is used on an online service deployment model as Markov Decision Process (MDP). The study effectively reduces service delays and improves cost efficiency in 6G networks by optimizing service deployment in multi-layer edge networks. \\ \hline \multirow{4}{*}{[35]} & \multirow{4}{*}{ICTACT Journal on Microelectronics} & \multirow{4}{*}{2022} & The aim of this study is to eliminate the interference in 5G ultra-wideband communication antennas and enhance performance in the cloud computing networks. This is achieved by proposing a novel Ultra-Wideband Communication Antenna (UWBCA) design with a ‘double square’ girder structure. This results in reducing latency, and improving spectral efficiency, data rates and connection density over existing antenna models. \\ \hline \multirow{4}{*}{[36]} & \multirow{4}{*}{Sensors} & This study aims to improve eHealth IoT system performance by integrating 5G network slicing, cloud computing, and fog computing. This is achieved using an IoT-fog–cloud architecture queuing model analysis employing performance metrics. Through resource allocation optimization with virtual BBU and fog nodes, the study improves throughput, QoS, and minimizes latency in 5G eHealth applications. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._