# Machine Learning-Driven Dynamic Traffic Steering in 6G: A Novel Path Selection Scheme
## 3 Network Architecture, Learning Framework, and Methodology
* The execution of the optimization solver where the objective function is to find the maximum matching values between clusters and transport attributes. The outcome of the process yields a decision for the best traffic-transport assignment. * The storing process of pertinent data is based on the outcome of the prior activities. * The execution of a supervised learning process on the historical data by train and test process to create an extensive classification model. The vision of the machine learning classification model is to provide an inference to be programmed in the nodes for traffic steering decisions. * A final output comprises hyperparameters and labels results from clustering and optimization solver activities. The machine learning algorithms rely heavily on the volume of data. 5G and beyond are data-driven architectures that utilize multiple data sources from different network functions and domains for automation, optimization, and improvement to support critical requirements, specifically in 6G [9]. From the storage activity (Step 6) in the workflow, the UE's generated data are used, and attributes like downlink, uplink, delay, and error rate values are captured and stored in every instance. Table 1 indicates the parameters involved in the study. Three different datasets are captured for every defined duration in three different timeslots to demonstrate the independent relativity of the classification model. \begin{table}
\begin{tabular}{c c c} \hline \hline
**No** & **Parameters** & **Remarks** \\ \hline \multirow{2}{*}{1} & \multirow{2}{*}{Total No. of UEs} & Total UEs: average UEs (5379) \\  & & 1 st Instance = 5443 \\  & & 60 th Instance = 5250 \\ \hline \multirow{4}{*}{2} & \multirow{4}{*}{Traffic Classification} & Based on the randomly generated percentage applied to the total number of UEs. \\  & & Sampled from 1 st instance. \\  & & 1. & Normal UEs: 2352 \\  & & 2. & UE classified in Specific 5G Use Cases: 3091 \\  & & 1. & Total eMBB UEs: 1241 \\  & & 2. & Total URLLC UEs: 250 \\  & & 3. & Total mMTC UEs: 1600 \\ \hline \multirow{2}{*}{3} & \multirow{2}{*}{Types of Transport Network} & 1. & Optical Fiber Network \\  & & 2. & Satellite Network \\  & & 3. & DOCSIS Coaxial Cable TV \\ \hline \multirow{2}{*}{4} & \multirow{2}{*}{Size of Datasets (based on duration)} & Three different datasets for 60, 300, and 600 instances, respectively. \\ \cline{3-4}  & & Unsupervised learning: multi-output classification using Random Forests algorithm. \\ \cline{3-4}  & & 1. & Dataset, \(I\), with the sample size of duration, \(t\), and users, \(u\). \(I\) e \(R\)\({}^{\text{txu}}\). \\ \cline{3-4}  & & 2. & User, \(u\), with features, \(f_{m}\), where m = [\(DL\)/\(UL\), \(pdb\), \(per\)]. \\ \cline{3-4}  & & 3. & Input: \(u\) e \(I\). \\ \cline{3-4}
5 & \multirow{2}{*}{Machine Learning} & \multirow{2}{*}{IV. Output: Multiple target variables, \(v\), denoted by cluster number, \(cn\), and transport networks, \(tn\).} \\ \cline{3-4}  & & & DL: Downlink \\ \cline{3-4}  & & & UL: Uplink \\ \cline{3-4}  & & & pdb: Packer Delay Budget \\ \cline{3-4}  & & & per: Packet Error Rate \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The parameters and attributes for traffic and transport attributes. Two new columns are added based on the output from Step 3 and Step 5 from Figure 2, representing the cluster group of UEs and the types of transport assigned. Step 3 uses raw data as an input, transforms the data into the two-dimensional form and executes a clustering technique to identify the hidden pattern based on the density of every point tabulated on the graph. It is clustered into three defined clusters to be mapped using the total number of transport types. Subsequently, each cluster is mapped to every possible pair between cluster, \(cn\), and the transport network, \(tn\), types. The highest matching score for traffic-transport represents high similarity, and it will be selected for the traffic-transport assignment process using an optimization solver to obtain the best matching cluster-transport pair.