# Intelligent Traffic Engineering for 6G Heterogeneous Transport Networks
## 4 Proposed ML Techniques, MILP Framework, and Methodology
### Clustering Process  
This study examined the functionality of unsupervised ML under clustering algorithms. The clustering algorithms were conducted with unlabeled data, in which the attributes of each data point were analyzed, and the similarity in a cluster was exhibited. An emulator that emulated the generated inbound and outbound data transacting from UE to gNB and the core (including vice versa) was used to derive the data for this study. These data included the Reference Signal Received Power (RSRP), DL, UL, and QFI values. A clustering algorithm was employed to learn from each data point and generate a vector value, \(D_{j}(t)\), representing a point's attributes or properties. Subsequently, the process combined all points with similar attributes into clusters.  
A different perspective on the resource assignment was also obtained based on different input types, and this study performed traffic clustering based on the use cases of the traffic. Three use cases were considered in this study: eMBB, URLLC, and mMTC. These use cases were represented with \(F_{i}(t)\), where \(i=\) [_eMBB_, _URLLC_, _mMTC_]. The datasets were classified into three categories, resulting in a format that resembled the previous cluster format, \(F_{i}(t)\):(\(V_{dl/ul}\), \(\epsilon_{min}\), \(\beta_{max}\)). This process asserted uniform traffic types within the group, contradicting the clustering process performed using the ML clustering technique. The clustering process then had to undergo another unsupervised learning technique for detailed execution.  
The dimensionality reduction algorithm produced two-dimensional data from the multivariate data. Before clustering, this process reshaped the data into a vector value using the t-distributed stochastic neighboring embedding (T-SNE) method. The data were filtered based on the referenced signal receive power (RSRP) values to exclude the farthest UE from the serving radio unit (RU). Only the UE inside the serving area was considered in the subsequent stages (step 3 in Figure 4). Finally, the filtered list of downlink and uplink values with QoS Flow ID (QFI) underwent the T-SNE transformation process to create an unlabeled dataset before the data execution for the clustering process (step 4 in Figure 4). The multivariate data from the prior processes underwent initial steps of scaling and transformation to produce a standardized scale to enable the balance dominance of every dataset field before applying T-SNE to reduce the multivariate dataset into a single unlabeled value.  
A clustering process was performed using Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) to identify patterns from the T-SNE transformation output. The clustering process identified the attributes of each point and grouped them into three clusters: \(T_{j}(t)=D_{\texttt{C}\texttt{\#1}}(t)+D_{\texttt{C}\texttt{\#2}}(t)+D_{\texttt{C} \texttt{\#3}}(t)\). Each point (represented as UE) was assigned a cluster label (C#1, C#2, and C#3) after the clustering procedure was completed, indicating the group of each point (UE) (steps 5, 6, and 7 in Figure 4).  
Subsequently, each label on a UE was stored and recorded for future data training and learning processes. The post-clustering process then shifted to the resource assignment stages. Steps 8 and 9 were the preprocessing stages before the resource assignment step. During this stage, the data were collected and converted into a matrix format. The transport network data comprised capacity information, round trip time, and packet loss rate, representing a transport network attribute value in a particular instance, \(\lambda_{\texttt{\tiny{E}}}\):(\(V_{d||t}\), \(\epsilon_{min}\), \(\beta_{max}\)).