### Recent Studies and Insights
\\ \cline{1-1}  & \multirow{9}{*}{[24]} & \multirow{9}{*}{IEEE Conference} & \multirow{9}{*}{2024} \\  & & & & \\ \cline{1-1}  & & \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Technology** & **Ref.** & **Source (Journal)** & **Year** & **Contribution** \\ \hline Digital twin [25] & Sensors & 2022 & The objective of this study is to enhance the positioning accuracy of UWB technology in indoor environments by developing a DT based indoor positioning system that addresses challenges posed by NLOS and LOS indoor obstructions. This is achieved using Slime Mould Algorithm (SMA) for optimizing position of anchor point. Employing a simulated model of anchor placement with Slime Mold Algorithm and addresses every network’s error utilization through the implementation of neural networks. This approach enhances UWB indoor positioning systems accuracy by improving anchor point positioning and handling errors in LOS and NLOS situations. \\ \hline
[26] & IEEE Internet of Things & 2022 & The objective of this study is Reducing total MAC layer latency and improving reliability in Beyond 5G (B5G) networks. To achieve this, a Reinforcement Learning (RL)-enabled MAC scheduler is applied, using the UCB1 algorithm. The study successfully lowers MAC layer latency and improves reliability in B5G networks by optimizing resource allocation and queuing for heterogeneous traffic. \\ \hline
[27] & IEEE BITS the Information Theory Magazine & 2022 & This objective of this study is to demonstrate how ML can be used to bypass traditional channel modelling to optimize wireless communication system design. This is achieved through the use of Deep Neural Networks (DNNs) and end-to-end training to directly optimize system-level objectives. The study enhances system performance by optimizing RIS reflection coefficients, distributing source coding for massive MIMO and mmWave initial alignment, and solving data-driven optimization problems. \\ \hline AI & IEEE Communications & 2023 & This paper attempts to solve bandwidth, privacy, data security, and inconsistent internet speed for Federated Learning (FL) for the Internet of Things (IoT). This is achieved by introducing a new framework based on 6G technology called super wireless over-the-air FL. Using interference resistant radio waves, the framework provides privacy protection for enhancing AI performance, ensuring that IoT system is smarter, faster and safer. \\ \hline
[29] & IEEE Access & 2024 & The aim of this work is to determine the optimal beamforming angles for transmitters and receivers in 5G and 6G wireless communication systems while reducing computation complexity. To achieve this RL algorithms were applied such as Q Learning, SARSA, Double Q Learning, and Expected SARSA. With enhanced data throughput and avoiding interference, the study effectively reduces complexity and improves channel capacity. \\ \hline
[30] & IEEE Networks & 2022 & The objective of this study is improving the quality of service (QoS) decision-making by preventing traffic congestion in Mobile Networks (MNs). This is achieved by applying Convolutional Neural Networks (CNN) which directly maps the condition of the MN to optimized admission control strategies. The model uses real world data from a telecom operator to enhance decision-making processes to help network operators make better informed more intelligent and timely decision to reduce congestion and improve QoS of user. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._