# Modelling of ML-Enablers in 5G Radio Access Network-Conceptual Proposal of Computational Framework
## 3 Applying AI/ML into 5G RAN: Proposed Methodology
### Data Processing/Weighting  
The differentiated purposes of the optimization purposes can be handled by adopting the parametrization of the input data to the ML algorithm, based on the ANN's model. Data collection can be formed as a sequence of layers, representing 5G traffic performance (user-centric) and RAN optimization use case (network-centric) generally denoted as:  
\[Hidden\ Layer\ Data=f(A,B) \tag{8}\]  
A hidden layer can represent a layer in between input layers (data set A and data set B) and output layers (prediction of 5G traffic characteristics and prediction of RAN optimization characteristics). Emerging from this deep learning setup, the artificial neurons implicate any combination of the parameters belonging to data set A and data set B. An algorithm in the network can apply the function as a set of weighted parameters and produce an output through the activation of a model with a complex data relationship. Ac \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Radio Conditions** & \(RSRP\) **(dBm)** & \(RSRQ\) **(dB)** & \(SINR\) **(dB)** \\ \hline Excellent & \(\geq\)\(-80\) & \(\geq\)\(-10\) & \(\geq\)20 \\ Good & \(-80\) to \(-90\) & \(-10\) to \(-15\) & 13 to 20 \\ Medium & \(-90\) to \(-100\) & \(-15\) to \(-20\) & 0 to 13 \\ Poor & \(\leq\)\(-100\) & \(<\)\(-20\) & \(\leq\)0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Key performance indicators value ranges for coverage and mobility optimization use cases. tivation of the function for generic RAN optimization, with equal importance of measuring coverage, mobility and \(QoS\) would target, in the intermediate layer, all key performance measurements taken per RAN optimization use case (according to Table 2: \(RSRP\), \(RSRQ\), \(SINR\), interruption time, latency, and throughput) with equal weight (e.g., \(w_{4}=1\), \(w_{5}=1\), \(w_{6}=1\)). The function applied to discover the relationship of the RAN optimization use case with \(eMBB\) service would take a set of \(eMBB\)-specific parameters (according to Table 1: peak data rate and user experience data), equally weighted too (e.g., \(w_{1}=1\)). For this particular scenario, the applied function can calculate the hidden layer with suppressed relationship for \(URLLC\) and \(mMTC\) services, by weighting the relevant parameters with zero (e.g., \(w_{2}=0\) and \(w_{3}=0\)). In this novel setup, defining ANN principles for running the ML model in 5G RAN, input, and hidden layers formed by sets of measurement quantities performed according to the measurement configuration are distinguished (see Figure 5). The configuration message sent to the UE through RRC signaling determines and requests the intended datasets. The specific performance indicators measured by the UE and received by the RAN can be weighted and treated as hidden layers (Figure 8). The input layer corresponds to the generic classification of the two main streams (5G traffic performance input data A and/or RAN optimization input data B). The individual metrics in the preceding and the succeeding sets of the hidden layer are in this model connected by weighted connections that facilitate datasets training according to the varying importance of the key performance indicators in the undertaken evaluation process. Figure 9 depicts what such dataset processing may look like. Let us assume the optimization purpose is RAN coverage optimization use case for an \(eMBB\) user. At the first iteration of ML model training, the RAN takes an action to order a dataset for a specific user, with \(eMBB\) service, for which good coverage should be ensured. To facilitate such action the UE may be provided by the RAN with the RRC configuration to collect \(RSRP\), \(RSRQ\), \(SINR\), peak data rate, and experienced data rate. With the MDT feature, the UE can report the metrics periodically in real time or log the measurements to provide periodic data collectively in a deferred way. Figure 8: The model for training data collection in different 5G usage scenarios.