# Beyond REM: A New Approach to the Use of Image Classifiers for the Management of 6G Networks
## 3 Proposed System
### Generation of Synthetic Network Images
Additionally, advanced image processing techniques can be employed to enhance the visual perception of the synthetic image, facilitating further analysis. The NSI generation process can be summarized in the pseudo-code algorithm provided in Algorithm 1. ```
Input:\(\Delta t\) [Temporal resolution (seconds)], \(\Delta p\) {Spatial resolution or pixel size (meters)}, Geolocated traces from UEs Output: Network State synthetic Image procedureGenerateNSI  Pixelize the area of interest using defined spatial resolution. for each UE measurement \(LM_{UE_{i}}(T)\) in the collected data do Locate the UE in the corresponding pixel \(P_{i,j}\) based on geographical location. Add the UE's KPI measurements \(KPI_{UE_{i}}(T)\) to the set of LMs \(LM_{P_{i,j}}\) for that pixel. for each pixel \(P_{i,j}\) in the pixelized area do MergeLM(\(LM_{P_{i,j}}\)) to calculate the aggregated KPI values \(K\tilde{P}I^{1}{}_{P_{i,j}}\), \(K\tilde{P}I^{2}{}_{P_{i,j}}\), and \(K\tilde{P}I^{3}{}_{P_{i,j}}\) using statistical method (e.g., average). LMCoding(K\(\tilde{P}I^{1}{}_{P_{i,j}}\), \(K\tilde{P}I^{2}{}_{P_{i,j}}\), \(K\tilde{P}I^{3}{}_{P_{i,j}}\)) to calculate the image format values \(R_{P_{i,j}}\), \(G_{P_{i,j}}\), and \(B_{P_{i,j}}\) for the pixel using normalization. GenerateNSI the synthetic image by combining image format values for all pixels. ```  
**Algorithm 1** NSI Generation. A key challenge with DL methods lies in their requirement for a vast quantity of training images. The proposed approach addresses this by systematically collecting geotagged and timestamped traces over varied time periods. This allows for a robust collection of NSIs to be created from this comprehensive and diverse dataset. NSIs serve as precise depictions  
Figure 2: NSI with RSRP, RSRQ, and SINR mapped as RGB with varying number of samples (e.g., time resolution). of different cellular network operating conditions, offering a rich training dataset for DL models. This method enables detailed network state representation, accounting for both spatial and temporal dimensions. Balancing spatial and temporal resolution is vital to avoid data overloading and visual clutter. Adjusting these variables can create a more thorough, relevant, and accurate network coverage image. This image can significantly aid in identifying and resolving network problems, enhancing overall performance and user experience. A critical aspect of this approach is the labeling of NSIs. Here, expert knowledge is pivotal, with network specialists providing labels based on their deep understanding of network states or failures. This process helps establish a reliable ground truth for training DL models, enhancing their ability to interpret new NSIs in an operational setting accurately. In some cases, specific failure data may not be readily available, which presents a challenge for labeling. To overcome this, the approach can employ artificial failure traces creation [27]. Using established radio propagation and failure models, simulated failures can be incorporated into NSIs, allowing for a more representative dataset. This innovative method enables DL models to learn and identify a broader range of failure states, improving their predictive capabilities and overall performance. Moreover, its compatibility with unsupervised learning methods demonstrates the proposed approach's versatility. While supervised learning with labeled NSIs can provide excellent results, unsupervised methods also have unique advantages. By applying unsupervised techniques to NSIs, the system can extract valuable insights and hidden patterns from data without explicit labeling. This feature further enhances the proposed approach's flexibility and strength, making it a versatile tool for handling 6G network complexities.