# Machine Learning-Driven Dynamic Traffic Steering in 6G: A Novel Path Selection Scheme
## 3 Network Architecture, Learning Framework, and Methodology
Thus, variables, \(v\in[cn,tn]\) and the \(V\) is the predictive value of \(v\) for each input of a user, \(u\), I; therefore, \(u\in I\). Table 2 enlists the activities in prior processes (from Step 1 to Step 5), and this study simplified the process to a minimum number of processes, reducing the handling time. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No** & **Activity** & **Output** & **Source/Process** & **Remarks** \\ \hline \multirow{4}{*}{1} & \multirow{4}{*}{Data Simulation, [14,15]} & List of Parameters: & & \begin{tabular}{c} Python-based simulator generating users \\ **Input**: generate UE and traffic dataset, \(I\). \\ **Output**: Parameter values for clustering. \\ \end{tabular} \\  & & i. & \begin{tabular}{c} Downlink, \(DL\) \\ **iii**: \\ **iv**: \\ **v**: \\ \end{tabular} & Simulation & Step 1: Define UE types. & - User, \(\text{u}_{\text{p}}\), \(p\), traffic classification types. \\ \cline{1-1}  & & & \begin{tabular}{c} Step 2: Define UE mobility pattern. \\ **v**: \\ \end{tabular} & \begin{tabular}{c} Define UE mobility pattern. \\ **-** Dynamic and static types of users. \\ **Step 3**: \\ **Step 4**: \\ \end{tabular} \\ \hline \multirow{4}{*}{2} & \multirow{4}{*}{Data Collection and Transformation} & \multirow{4}{*}{TSNE two-dimensional data format} & \begin{tabular}{c} Unsupervised \\ learning: \\ Dimensionality \\ Reduction \\ \end{tabular} & \begin{tabular}{c} User, \(\text{u}\) with features, \(f_{\text{m}}\), where m = \{DL/UL, pdb, per\} \\ transformed to \(u_{\text{tot.size}}=[x_{i}]\). \\ \end{tabular} \\  & & & \begin{tabular}{c} **Input**: UE and traffic dataset, \(I\). \\ **Output**: UEs in three clusters, \(C_{\text{m}}\). \\ \end{tabular} \\ \cline{1-1}  & \multirow{4}{*}{Clustering} & \multirow{4}{*}{\begin{tabular}{c} Three clusters consist of \\ UE packets \\ \end{tabular} } & \begin{tabular}{c} Unsupervised \\ learning: \\ HDBSCAN clustering \\ \end{tabular} & \begin{tabular}{c} Step 1: Measure the distances between points, \(d_{\text{dist}}=1x_{\text{curr}}\) \\ \(-x_{i}\,|\). \\ \end{tabular} \\ \cline{1-1}  & & & \
\begin{tabular}{c} Step 2: Define HDBSCAN core and minimum samples and \\ cluster size. \\ \end{tabular} \\ \cline{1-1}  & & &
\begin{tabular}{c} Step 3: Visualize the clusters. \\ **Step 4**: \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The detailed activities in prior works. A Python-based program simulated the UEs and the traffic generated by the UEs. It generated various types of traffic/users scattered and served in cells converging to an access node, as in Figure 1. In an area, the simulator emulated the generations of (1) static and mobile users and (2) the classifications of traffic produced by each user. The traffic generated by UEs followed the attributes of eMBB, URLLC, and mMTC categorized by the size of DL and UL data, packet error rate, and packet delay budget. Supervised learning works with models were created from the training and test process of data. However, this study explored creating a model based on splitting the data according to the ratio in Table 3. This step explored the granularity of the data size, where the models were developed by splitting the data based on percentages in Table 3. The volume of each data set was determined by the duration of the recorded data and the total number of UEs in each instance. Referring to Figure 3, the volume of each instance is huge because of the high number of UEs (more than 5000 UEs) with diverse attribute values. The multiplication of the total number of UEs with total instances in each duration tabulated in Table 3 possesses a weightage to be measured from this perspective. Models denoted by \(M_{ij}\) represent the volume of data used to build the model. Subsequently, a series of \(M_{i4}\) models was used to train different data sets. Next, the selected model \(M_{i4}\) from Table 4 was used to classify the cluster number and the assigned transport network.