# Assessment of Deep Learning Methodology for Self-Organizing 5G Networks
## 6 Comparative Analysis of Proposed Framework with the Existing Frameworks  
In this section, we explain some of the advantages and/or disadvantages of adopting the proposed autoencoder-based machine learning framework. Although nearest neighbor based approaches like k-NN have been widely used and accepted as easy methods, they lack the ability to be predictive. Nearest neighbor approaches cannot be extended for sequential data in the true sense, because they require a batch of data to be trained on. Nearest neighbor approaches are suitable for high dimensional data but work well for low dimensional data. In anomaly detection, nearest neighbor approaches require the calculation of some kind of score that is usually computationally expensive. On the other hand, artificial neural network-based approaches, like the autoencoders explored for cell outage detection in this work, are able to overcome all of the above-mentioned drawbacks. In general, ANNs have the ability to store information about the entire network, and missing information in some places does not stop the network from functioning. Once trained ANNs are able to handle missing values in the test data, the loss of performance only depends on the importance of the missing information. The structure of ANNs can be exploited in many ways, and, similarly, training the network has a large degree of freedom (DoF). ANNs learn from events and have the ability to make decisions. They also have parallel processing capability and can be run simultaneously to perform several jobs at the same time. On the other hand, the degree of freedom in the structure and hyper-parameter space requires that such parameters are properly optimized and/or that a trial and error strategy is adopted. This results in excessive training in terms of computational time and resources. ANNs also have a strong hardware dependence, especially deep learning, where the models have large structures. Sometimes, due to unexplainable behavior, it is difficult to explain why and how the solution was attained. This is an important drawback because it reduces trust in the network. ANNs work with numerical data, and all other forms of data need to be transformed to numerical values. The Table 3 shows a comparison of some of the attributes discussed with regard to standard nearest neighbor approaches and the neural network-based approaches that are used in deep learning. Table 3, compares the requirements of PCA/SVD, the requirements of explicit score computation, the reliability in the presence of missing information, the degree of freedom (DoF), the amount of data utilization, and the ability to extend and utilize high performance computation (HPC). Due to their strength in terms of performance on very difficult problems, deep neural networks are the state-of-the-art methods. Research and development in ANNs is occurring at an extremely fast rate in the present day. The disadvantages of ANNs are quickly being reduced day-by-day, while their advantages are increasing. Deep learning and AI will be the technology of the future, and future cellular networks will require that AI is built along with them. When compared to other frameworks, the proposed framework mainly has advantages in terms of its simplicity and the capability to to be extended with other methods. This is also the case for \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**PCA/SVD** & **Score Computation** & **Missing Data** & **DoF** & **Data Utilization** & **Extendable** & **HPC** \\ \hline Nearest-neighbors & ✓ & ✓ & ✗ & Small & Limited & ✗ & ✗ \\ \hline Neural Networks & ✗ & ✗ & ✓ & Large & Extensive & ✓ & ✓ \\ \hline Clustering & ✓ & ✓ & ✗ & Small & Limited & ✓ & ✓ \\ \hline Support Vector & ✓ & ✓ & ✓ & Small & Limited & ✓ & ✗ \\ \hline Probabilistic & ✓ & ✓ & ✓ & Small & Limited & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of attributes of neural network (deep learning)-based approaches with standard nearest neighbor and other approaches.