# Beyond REM: A New Approach to the Use of Image Classifiers for the Management of 6G Networks
## 5 Conclusions and Outlook  
The work presented highlights the significant promise of Network Synthetic Images as an innovative tool for managing complex 6G mobile networks. The foundations of an NSI generation framework, formed from geo-localized traces, have been delineated, shedding light on the associated factors and their consequential impact on the processing timeline. The verification of the effectiveness of the developed approach in network fault management illustrates the precision of network state identification enabled by the careful optimization of image samples and coverage areas. The proposed framework, which integrates CNN-based classifiers for fault diagnosis, is shown to outperform traditional SVM-based approaches using RSRP map samples. The technique of creating synthetic images from KPI geo-localized traces, coupled with advanced image classifiers, paves an optimistic path towards the evolution of automated 6G network management. This method may potentially refine the currently complex network management processes that require intricate log analyses by specialists, further enabling the discovery of previously undetected issues. Overall, the results demonstrate the promising capability of the proposed framework in using location-based network data to predict and diagnose potential network issues effectively. The efficient and accurate diagnosis of faults, coupled with a low computational cost, makes this framework an attractive solution for improving the resilience of future networks. Despite the optimistic results, several opportunities for future exploration have been discerned to improve NSIs' efficacy and application range. Introducing additional data sources such as user mobility patterns or environmental factors may provide a more holistic representation of the cellular network, potentially increasing the accuracy of NSIs and overall network management. Continued exploration into advanced deep learning models such as Deep Neural Networks (DNN) or Recursive Neural Networks (RNN) may refine NSI generation and interpretation, potentially yielding superior performance in network diagnosis tasks. The proposition for future exploration also includes alternative management techniques that utilize geolocated mapping. The potential issue of data inadequacy might be alleviated by employing Generative Adversarial Network (GAN) techniques [32]. Future exploration could focus on feature engineering [33] to enhance the representativeness of NSIs, placing emphasis on the distinctive features of an area to visually augment its significance. Expanding the NSI framework to include multiple KPIs or other network performance metrics in a single synthetic image could provide a broader view of the network's state, thereby aiding the identification of complex problems or correlations among different network elements. The utilization of image treatment algorithms, such as filters for NSI post-processing, might enhance visual cues, thereby facilitating optimal training of machine learning mechanisms [34]. The development of methodologies for real-time updates of NSIs as fresh data become accessible could pave the way for more dynamic and adaptable network management, fostering swift responses to changes in the network environment. An evaluation of the proposed framework's scalability for large and heterogeneous networks, particularly the rapidly advancing 6G networks, is imperative to ensure future usability. Finally, the analysis of NSIs over time (in a multimodal format such as a movie) may provide network operators with invaluable insights for real-time monitoring, predictive analysis, automatic network diagnosis, and optimization. This could unlock a more efficient and zero-touch network management paradigm, fostering a seamless transition into the era of 6G. Conceptualization, E.B. and S.F. ; methodology, E.B. and S.F. ; software, F.M. and C.B. ; validation, S.F., F.M. and C.B. ; formal analysis, E.B. and S.F. ; investigation, E.B., S.F. and C.B. ; resources, R.B. ; writing--original draft preparation, E.B.