# Beyond REM: A New Approach to the Use of Image Classifiers for the Management of 6G Networks
## 3 Proposed System
### Deep Learning Inference  
Once a sufficient collection of NSIs has been established, these can serve as inputs for DL image analysis techniques. The choice of a specific DL algorithm largely hinges on the intended management task and the proper construction of the NSIs. The accuracy and efficiency of the analysis will be influenced by visual aspects such as shape and color. Among potential DL algorithms, Convolutional Neural Networks (CNNs) have revolutionized image classification, often delivering high accuracies. CNNs take an image as input, process it, and categorize it into specific groups. This process relies on \"convolution\", a technique that scans an image with a filter to produce a new, processed image. The network learns image features, starting from minute details in the initial layers to more generalized image features in the final layers. The CNN identifies image components by learning from a variety of filters across multiple layers; convolution pinpoints local patterns, while pooling detects global ones. CNNs are particularly effective at image classification due to their inherent ability to form spatial hierarchies of features through repeated application of convolutions and pooling operations. To tailor a CNN for specific network management tasks, such as self-diagnosis, the architectural design and the training dataset must be adapted. We explore these adaptations in more detail below:  
* **Visual Characteristics:** NSIs can depict specific sectors within the mobile network environment at a pixel level. Different color gradients or intensities correspond to radio parameters such as RSRP, RSRQ, and SINR, among other performance indicators. These visual traits encapsulated within the NSIs reflect various aspects of network performance and serve as input features for CNN. * **Adaptation of CNN Architecture:*
* The CNN architecture, aimed at network self-diagnosis, needs to be structured to capture pertinent features within the NSIs. This involves:
* Convolutional Layer Selection: The number, dimensions, and stride of the convolutional filters should be chosen wisely to extract spatial features from the NSIs. * Pooling Layers: Pooling layers can retain key features while reducing the spatial dimensions of the NSIs. In a self-diagnosis task, max pooling can be imple
mented to preserve the most significant signal features during the dimensionality reduction phase. * Fully Connected Layers: Given the dynamic and diverse 6G network environment, the number of neurons and layers within the fully connected part of the network needs to be optimized to handle the nuanced complexity of potential interference or power issues. * Output Layer: The output layer should be designed to provide the required classification output, such as identifying areas of weak signal quality or deducing the root cause of network failures. In NSIs, the colors or intensities can represent varying measured radio values, as well as indicators such as dropped calls or data throughput. Once synthesized, these NSIs can be used to train the CNN to classify regions into categories such as \"good coverage\", \"poor coverage\", \"interference\", or \"congestion\". Image classification DL algorithms can identify patterns beyond human perception, potentially highlighting areas of concern. An essential use case involves using historically labeled data to recognize network issues during a past failure or the preceding hours. This predictive capability enables the algorithm to provide proactive alerts to prevent network failures. The CNN architecture can be tailored to this problem using multiple convolutional layers with filters designed to capture spatial network performance patterns. Pooling layers could be added to help the network focus on larger-scale patterns, while fully connected layers could be tuned to provide the desired level of abstraction. The output layer could be designed to classify each region, enabling the network provider to identify and address performance issues rapidly.