# Beyond REM: A New Approach to the Use of Image Classifiers for the Management of 6G Networks
## 4 Evaluation  
The effectiveness and versatility of the proposed framework are assessed through the use of a comprehensive scenario adopted from [27]. This is facilitated by a system-level simulator that has undergone rigorous validation. The NSIs resulting from this scenario are shown in Figure 2. The scenario is inspired by the departure area of Malaga City Airport, which is characterized by its complex architectural design, including walls and boarding gates. The challenging environment includes 12 picocells scattered over an area of 200 \(\times\) 300 m, complemented by three macrocells, the closest of which is located approximately 500 m north-west of the highlighted building area. The validation of the results is strengthened by the use of the UMA-developed LTE simulator [28], which has been extensively supported in various studies. Users in the simulated scenario explore all reachable areas, replicating regular user density patterns at designated locations, such as security checkpoints and boarding gates. Network measurements encapsulate cell-level metrics and direct UE radio reports, reflecting those in actual deployments. Radio-based indicators allow a quick response to possible network faults, eliminating the need for large numbers of users or long measurement durations. Key Performance Indicators (KPIs) are calculated as the average of collected UE measurements during each 60-s simulation loop, with a UE reporting interval of 100 ms. User movements are mimicked by a random waypoint-based model, which outlines user density hotspots in high-impact areas. Table 2 presents detailed simulation parameters that resemble real-world operational scenarios and provide strong validation of the proposed methodology. A range of conditions and primary failures are examined, including normal conditions (no failures), macrocell interference, picocell interference, and cell power degradation. The evaluation of the system involves the analysis of 4004 one-minute network simulation loops, representing almost three days of data. These loops consist of 25% simulating normal network conditions, 25% emulating macrocell interference, and the remainder split between picocell interference and power degradation. The goal is not to provoke network problems, but to create a model that is able to identify and correct naturally occurring problems, thereby boosting network resilience. A set of NSIs is produced, each employing one UE radio variable per RGB color layer (i.e., RSRP, RSRQ, and SINR), with the intensity being directly mapped based on the normalization of potential values. Three pixel sizes (1, 5, and 10 m) are selected, the choice of which hinges on the accuracy of the localization method in a real environment. As earlier stated, the acquisition time is directly proportional to the pixel size necessary to fill the majority of the image. This condition could lead to an insufficient number of samples for a given pixel size acquisition time, for instance, 10k samples with a 1 m size. During the evaluation process, different combinations of pixel size (in pixel edge meters) and the total number of NPI samples (ks) are implemented to build NSIs, ensuring that their total area covers the network's range. This method leads to four configurations: 1 m-100k samples, 5 m-100k samples, 5 m-5k samples, and 10 m-5k samples. The mean value of all samples falling within each pixel is taken to merge data. A four-layer Convolutional Neural Network (CNN) conducts image recognition, yielding four potential outputs corresponding to the labeled network states. Two baselines are compared: one applying a Naive Bayes Classifier (NBC) to RSRP maps and another utilizing the same map types with a Support Vector Machine (SVM) algorithm, as proposed in [29]. The k-fold cross-validation technique validates the ML models, where 'k' represents the dataset partitions. Here, 'k' is set to 5, signifying five-fold cross-validation. Each validation iteration trains the model on k-1 partitions (or folds) and tests it on the remaining one.