# Modelling of ML-Enablers in 5G Radio Access Network-Conceptual Proposal of Computational Framework
## 3 Applying AI/ML into 5G RAN: Proposed Methodology
### Outcome Evaluation  
The association of the input data with various optimization uses cases, followed by reliable feedback, intends to achieve a predictive outcome. The result aims at the prediction of 5G traffic performance and avoidance of the 5G network performance dropping below expectations. Such a procedure is built on preceding tailored datasets selection and their appropriate processing that enables observation of whether the expected performance targets are met. To provide a good prediction for the observed 5G traffic characteristics, the joined evaluation with intended RAN optimization use cases sets different targets. Table 4 summarizes the expected performance depending on the nature of traffic. It can be observed that the expected target values vary for the same 5G traffic type in different scenarios. For ML-driven 5G RAN evaluation outcome, it determines a factor to make a decision in a general structure of an ML-based predictive model for 5G traffic in a given RAN optimization use case.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Performance Indicator** & **Use Case** & **Target Value** \\ \hline Peak Data Rate & \(eMBB\) & Downlink 20 Gbit/s \\  & & Uplink: 10 Gbit/s \\ \hline User Experience Data Rate & \(eMBB\) & Downlink 100 Mbit/s \\ (Throughput) & QoS & Uplink: 50 Mbit/s \\ \hline Mobility interruption time & \begin{tabular}{c} \(URLLC\) \\ Mobility \\ \end{tabular} & 0 ms \\ \hline Latency & \begin{tabular}{c} \(URLLC\) \\ QoS \\ \end{tabular} & \
\begin{tabular}{c} Control Plane: 20 ms \\ User Plane: 1 ms \\ \end{tabular} \\ \hline Connection density & \(mMTC\) & 1 000 000 devices per km\({}^{2}\). \\ \hline Radio signal level & Coverage Mobility &
\begin{tabular}{c} \(RSRP>-90\) dBm \\ \(RSRQ>-15\) dB \\ \(SINR>13\) dB \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 4: Target values of the 5G key performance indicators depending on RAN optimization use case.  
In a combined evaluation methodology, where the evaluation is applied to a set of use cases, the outcome can be classified according to a decision tree using a random forest (RF) classifier. This decision tree is developed as an ensemble classification technique that uses \"parallel ensembling\" which fits several decision tree classifiers in parallel on different data set subsamples, and uses majority voting or averages for the outcome or final result [11]. Such methodology in RAN can apply to a generic network performance assessment, without strict focus or strength requirements for a specific optimization use case (coverage, mobility, or _QoS_). In the scenario, where datasets are produced as inputs from a few domains and overall, reasonable performance is deemed. The random forest-based algorithm establishes a baseline to predict averaged and aggregated assessment (Figure 10).  
The ANN model can be further considered beyond observability of an individual user or a single area unit. As studies in [18; 19; 20; 21; 22; 23] have shown, applying convolutional neural networks (CNN) and graph neural networks (GNN) in the wireless network may reveal abilities to achieve optimal radio resource allocation in the network, predict channel stability, minimize spectrum allocation costs and maximize radio resource utilization. [18] and [19] teach CNN use for network slicing.  
Once the data collection from the above method limits the input data function to data set A, it can be considered jointly with methodologies developing ML-based radio resource allocation in the network, as in [19]. It can offer a means to minimize spectrum allocation costs for the tenants of selected slices and guarantee desired service level agreements (SLA).  
If suitably defined, network slicing observability and optimization can be applied together with GNN-based methods [20]. Graph-based deep learning can help to employ visualization of end-to-end network performance [21].