## 2 Literature Study  
Since the introduction of the first generation (IG) in 1979, mobile communication systems have revolutionised various sectors, including social and education. However, as the number of sectors and applications that rely on these communication systems expand, the systems face challenges in meeting user demands regarding resources, particularly in QoS provisions and efficient spectrum utilisation. Therefore, planning resources is crucial and can be simplified using an ML algorithm to automate the challenging process. A study proposed by [PERSON] et al. concentrates on base station beam selection using semionline learning, which considers the traffic environmental variations for locating beams [8]. Moreover, [PERSON] et al. [9] proposed a resource allocation policy framework using a deep neural network (DNN) for dynamic vehicular networks. The proposed framework was implemented in software-defined networks (SDNs), which demonstrated the performance of 5G system data centres and carrier networks. However, the DNN cannot compete in terms of time and precision in resource distribution. ML and deep learning (DL) can also be used to detect irregularities in the Internet of Things (IoT) applications. The results of combining these strategies are better than those of using only one DL algorithm. In [10], several deployment and analysis approaches of the intrusion detection system (IDS) were investigated using the IoT architecture. The most advanced DL models available include recurrent neural network (RNN), convolutional neural network (CNN) and a combination of the two, with RNN and CNN as the most sophisticated systems involving a large number of tuning parameters and a long time to obtain proper optimisation [11]. Moreover, processes such as data gathering, integration and forecasting are the key components of a framework for resource management and network optimisation to ensure a proper prediction model in 5G. However, problems and challenges, such as extracting con \begin{table}
\begin{tabular}{c c} \hline \hline
**Acronyms** & **Definition** \\ \hline
5G & Fifth Generation \\ CART & Classification and Regression Trees \\ CNN & Convolutional Neural Network \\ DL & Deep Learning \\ DNN & Deep Neural Network \\ EDRP & Edit Distance with Real Penalty \\ FML & Fast Machine Learning \\ KNN & K-Nearest Neighbour \\ LCS & Longest Common Subsequence \\ MDT & Minimization of Drive Test \\ ML & Machine Learning \\ QoS & Quality of Service \\ RF & Random Forest \\ RNN & Recurrent Neural Network \\ SCOC & Small Cell Outage Detection \\ SDN & Software-Defined Networks \\ SVM & Support Vector Machine \\ VANET & Vehicular Ad Hoc Network \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Acronyms and definition. texts from online data and simulating proactive optimisation still exist [12]. Therefore, fast ML (FML) was proposed as a method for adapting 5G vehicular ad hoc network (VANET) base stations [13]. Based on the results, FML outperforms the benchmark algorithms in terms of performance and adaptability. The findings of the online bandit learning imply that context awareness is critical in 5G situations. Creating a hybrid system for tracking individuals whilst increasing total network capacity may be important in the future [14]. Moreover, ML techniques can be utilised to create a self-organising network, which can improve network performance, reduce network downtime and increase user experience over cellular networks. The Bayesian ML approach was proposed by [PERSON] et al. [15] as a novel method for optimising the performance of SDNs. In this method, each node should detect the underlying stochastic process utilised by the controller to create the flow rules and predict the relevant rules for packets that do not meet the flow table. Furthermore, a small cell outage detection (SCOD) strategy was presented in [16], based on the partial key performance indicator (KPI) data from an extensive collection of Minimisation of Drive Test (MDT) reports.