# Assessment of Deep Learning Methodology for Self-Organizing 5G Networks
## 4 Enabling 5G with SON and Deep Learning
### Deep Learning-Based Mobility Load Balancing  
A cellular network can suffer from a huge amount of traffic on a few nodes or cell congestion. In order to solve such issues, SON deploys mobility load balancing (MLB) approaches as part of the self-optimization. MLB enables load transfer between cells to improve the service quality. As challenges are posed by 5G to cellular networks, the importance of MLB becomes even greater. Moreover, 5G will consist of dense networks, meaning an increase in data, while the number of users is projected to grow as well. Previously, networks have used certain measures of different resources to determine the load of a cell, for example, the total transmitted power and total received power, interference in a cell, the handover failure rate, and the cell throughput. These kinds of variables are used usually in SON as well to analyze or determine whether the load balancing mechanisms for a cell must be activated. A drawback to these approaches is that they are generally reactive approaches, while in 5G, more proactive approaches are required so that the latency in the management is reduced. A major advantage of using machine learning, especially, artificial neural network-based approaches, is that they can predict future congestion. This kind of approach can be implemented by cellular traffic monitoring at the cell level or base station level, and then a deep neural network model can be trained to learn traffic patterns. The model would then be able to predict future traffic patterns or indicate cell congestion prior to the problem. This would allow load balancing mechanisms to be implemented early, thus resulting in a reduction in latency. A wide range of approaches has been used for load balancing; the reader is referred to a few [70; 71; 72].  
The current load balancing mechanisms in cellular networks are based on different methods, for example, changing the coverage area of the cell depending on the load variation. Usually, the transmission power is increased or decreased to expand or reduce the cell coverage area. Other techniques include the use of antenna tilt schemes or handover parameter modification. The MLB is based on finding the optimal handover (HO) offset value between an overloaded node and a target node. This is required so that the users can be shifted to less loaded neighboring cells that are adjacent to the loaded cell. However, this is also a complex mechanism as the adjacent cells might not have enough capacity to handle additional cellular traffic. Again, in such scenarios, deep learning-based approaches could be useful due to their predictive ability.  
Current state-of-the-art MLB approaches use reinforcement learning methods to dynamically adjust handover parameters. Such methods also learn with reward-based schemes. The problem is an optimization task and the parameters are adjusted according to certain predefined metrics. The load of the network is constantly monitored. If there is an increase in the load of a cell, then the network parameters can be adjusted to mitigate the problem. Deep learning based approaches can enhance the MLB methods in 5G by implementing the traffic pattern monitoring and congestion detection schemes [73; 74].