# Assessment of Deep Learning Methodology for Self-Organizing 5G Networks
## 4 Enabling 5G with SON and Deep Learning  
There has already been a lot of work done on the use of machine learning approaches in the cellular network domain [54; 55; 56; 57; 58; 59]. In this section, we describe how SON and deep learning could play an important roles in jointly driving future cellular networks [60]. When the 1G mobile networks were deployed, they made the connection between people instead of places, as was done in predecessor technology, possible. Similarly, when deployed, 5G mobile networks will make the connection between anything possible, instead of just people [61]. Three key areas have been identified where major changes will occur with 5G. The networks will utilize mmWave, massive MIMO, and densification [62]. 5G networks are more about D2D communication, which means there will be a higher number of users and more demand for capacity. An effective way to increase capacity is by making the cells smaller, which has also been a trend in the past. This leads to densification, and it is projected that to enable the kind of capacity required, there will need to be a 40-50-fold increase in densification [61]. This number far exceeds other sources of change in the mmWave and massive MIMO that will be deployed in 5G. Therefore, SON and deep learning will have the highest potential impacts on 5G.  
The dynamic and unpredictable factors involved in 5G networks, for example, spikes in social media usage, can occur at any time, demand fast and efficient automation. The task of SON and deep learning is to transform data/Key Performance Indicators (KPIs) into more actionable information [63]. In general, ML consists of predictive analytics that is able to determine the probability of future events or the likelihood of any particular event occurring [63]. Although, SON automates a lot of the functions required for the management and allocation of resources, there are still problems that are solved by applying a set of rules of thumb. These rules are derived from experience and domain knowledge. SON implements a level of intelligence in the system using programmed rules and, to a lesser extent, by using ML. However, this method needs to improve due to the increase in the number of challenges faced from the dynamic and diverse traffic and network complexity.  
In the current networks, monitoring relies on alarms generated at the cell level based on static thresholds. This is inefficient for 5G, which will have a dynamic nature [63]. Engineers depend on troubleshooting guides; however, there are obvious limitations to such approaches, because such guidelines implement the one-size-fits-all criteria. Such guides are only limited to, or useful for, known issues, but the future networks need to be adaptive. The time taken to resolve issues causes latency and results in customer dissatisfaction. The time taken to investigate and resolve issues increases the cost as well. The growing network technology and complexity demands automated, flexible, energy efficient, low-cost, capable, and scalable solutions [63]. In addition, the network has to be capable of continuously learning from the environment and improving its performance in mobile data delivery in terms of the user context and network context. Networks using deep learning or AI are able to provide efficient solutions to some of these problems [64; 65]. Therefore, SON and deep learning could work together to resolve many of the dynamic problems in future cellular networks.