# Beyond REM: A New Approach to the Use of Image Classifiers for the Management of 6G Networks
## 4 Evaluation
Every iteration involves training for five epochs, using a batch size of 100. The models make predictions on the test set after training. These predictions form a confusion matrix, a tabular layout that visualizes an algorithm's performance through the \begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameter** & **Detail** & **Value** \\ \hline LTE Simulator Time resolution & Model & 100 TTI (100 ms) \\ \hline RRM model & Cell reselection & Criteria S, R \\  & Handover & Events A3, A5 \\ \hline Traffic model & Calls & Poisson (avg. 0.43 calls/user-h) \\  & Duration & Exponential (avg. 100 s) \\ \hline Mobility model & Outdoor & 3 km/h, random direction \& wrap-around \\  & Indoor & Random Waypoint based model with hotspots \\ \hline UE model & Noise figure & 9 dB \\  & Noise density & \(-\)174 dBm/Hz \\ \hline Base station & Directivity & Omni (small)/tri-sector (macro) \\  & Access & Open (small)/open (macro) \\  & Equivalent Isotropically & 3 dBm (small cells)/43 dBm (macro) \\  & Radiated Power (EIRP) & \\ \hline Propagation model & Indoor-indoor & Winner II A1 \\  & Indoor–outdoor & Winner II A2 \\  & Outdoor–outdoor & Winner II C2 \\  & Outdoor–indoor & Winner II C4 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation parameters. comparison of actual and predicted classifications. The final performance metric considers the mean performance across all five iterations. The state-of-the-art SVM method for Radio Frequency (RF) map classification, which exclusively relies on RSRP [29], attains an accuracy below 0.9. In contrast, the NSIs comprising three radio KPIs per LM encoded in RGB colors indicate potential improvement through the proposed framework. Evaluation outcomes suggest that changing the hyperparameters C, gamma, and kernel for the SVM-based model per each NSI grouping per scale leads to different results. The most optimal outcome is achieved through the CNN approach at a 10 m scale with only 5000 samples per NSI. This particular configuration exceeds an accuracy of 0.9, a significant accomplishment displayed in Figure 3.  
The F1 score formula is utilized to assess the performance of a supervised algorithm. This formula is defined as the harmonic average of precision and recall:  
\[\text{F1}=2\cdot\frac{\text{Precision}\cdot\text{Recall}}{\text{Precision}+ \text{Recall}} \tag{8}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{9}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}}. \tag{10}\]  
Here, TP stands for true positives, FP for false positives, and FN for false negatives. As confirmed in Figure 4, the generic CNN with RGB images surpasses alternative methods on all diagnosis indicators (precision, recall, F1), achieving a performance above 90% in all cases. A 5 m spatial resolution is determined to provide optimal output for the CNN algorithm, considering the reduced computational cost associated with lower spatial resolution. The application of NSIs derived from location data allows for the effective usage of image classification algorithms from low-precision positioned traces. Such NSIs can be further refined according to their intended application. In terms of training time, the proposed image-based CNN approach is superior to SVM and NBC. All algorithms and configurations are tested on an Intel Xeon processor server with eight cores and a processing speed of up to 2.4 GHz per core. As illustrated in Figure 5, CNN demonstrates shorter training times compared to SVM and NBC, barring the 10 m-5 ks configuration, where they are almost equivalent. Figure 3: Accuracy comparison. Although network states are equally represented in this scenario, data imbalance can present a considerable challenge, especially when handling rare fault states. The underrepresentation of these fault states in the dataset could affect the model's performance. Various strategies could be employed to address this issue.