### Recent Studies and Insights  
Supporting technologies including artificial intelligence (AI), cloud, and/or the digital twin technology (DT) are expected to contribute to the development of the coming 6G networks. Table 2 shows recent studies regarding the use of these technologies. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Technology** & **Ref. ** & **Source (Journal)** & **Year** & **Contribution** \\ \hline \multirow{9}{*}{[20]} & & \multicolumn{3}{c}{The aim of this study is to improve computational performance and network security in 6G Internet of Vehicles (IoV) environments. To attain this, this study proposes an AI enhanced DT framework incorporating an advanced feature engineering module comprising stacked sparse autoencoders (ssAE) for dimension reduction wherein the AutoFS and AutoCM specialize in an online learning module for accurate attack detection. With the proposed DT framework, the performance of attack detection increases significantly, while simultaneously reducing system latency, energy consumption and RAM usage while increasing packet delivery rates in dynamic 6G IoV networks. \\ \hline \multirow{9}{*}{[21]} & \multicolumn{3}{c}{The objective of this study is to develop a DT for NB-IoT wireless communication in an industrial indoor environment, and to analyze its performance when operating within real world conditions. To achieve this, simulations are conducted using Wireless InSite software to model and analyze critical communication metrics like throughput, SNR and received power. This study revealed that industrial environments reduce NB-IoT performance, with throughput 21% to 32% below peak rates. The results provide insights for improving communication efficiency in such settings. \\ \hline \multirow{9}{*}{Digital twin} & \multirow{9}{*}{[22]} & \multirow{9}{*}{IEEE Access} & \multirow{9}{*}{2023} \\  & & & \\  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \hline \multirow{9}{*}{Digital twin} & \multirow{9}{*}{[22]} & \multirow{9}{*}{IEEE Access} & \multirow{9}{*}{2023} & This study seeks to formulate a DT model aimed at unobtrusive patient respiration monitoring in healthcare 4.0. For this purpose, Wi-Fi Carrier State Information (CSI) is used and signal processing approaches like Elliptic Filters, Principal Component Analysis (PCA) to denoise raw data of the signal as well as Machine Learning (ML) algorithms for binary and multi-class classification such as Neural-Networks. To improve patient monitoring and decision support in healthcare settings, the study contributes to improving data processing and classification accuracy. \\ \cline{1-1}  & \multirow{9}{*}{[23]} & \multirow{9}{*}{IEEE Wireless Communications} & This study focused on the generative AI-enabled DT for wireless networks and propose a hierarchical design approach based on DT considering generative AI to handle challenges in 6G network architecture. This is accomplished using generative AI models such as Transformers and GANs to create message-level and policy-level DTs of the core network control plane. Through admission control and resource allocation, the study improves the stability and reliability of the network and proposes a scalable 6G network DTs-based framework. \\ \cline{1-1}  & \multirow{9}{*}{[24]} & \multirow{9}{*}{IEEE Conference} & The purpose of this work is to develop a wireless multi-hop network management system that continuously examines the network context in order to obtain network performance insight and suggest reconfiguration. To achieve this, the system combines simulations within the Click Router architecture, graph-based algorithms using the LEMON C++ graph library, and a theoretical link model to construct a Digital Twin Network (DTN) for optimizing Bluetooth Mesh networks. The DTN based system is demonstrated using testbeds which results in improved network resilience and management, especially for traffic scheduling in IoT constrained devices.