# Assessment of Deep Learning Methodology for Self-Organizing 5G Networks
## 4 Enabling 5G with SON and Deep Learning
### What is SON? Self-organizing networks or simply SONs are sets of automation techniques in mobile radio access networks or cellular networks. SONs can be intelligent in the sense that they can learn from their environments while autonomously adapting to ensure reliable communication. The purpose of SONs is to automate the processes of network planning, configuration, and optimization together in order to minimize the amount of manual work required [60]. As a result, the cost of management is reduced by simplifying operational tasks through self-configuration, self-optimization, self-healing, and self-protection [13]. The key SON objectives can be simply illustrated as  
* Improving the network capacity, coverage, and customer service experience by enhancing the network performance;
* Reducing the capital expenditure and operational expenditure (CAPEX/OPEX);
* Introducing autonomous and intelligent adaptability in the cellular networks through the self healing, self optimization, and self configuration properties of SON networks;
* Improving the spectral efficiency of the next-generation Radio Access Networks (RANs). 3 GPP introduced the SON as a vital component of the LTE network for the first time in Release 8. Cases of SON use in SOCRATES [66] are classified according to the life cycle of different phases within the cellular network. The phases of planning, deployment, maintenance, and optimization are classified as self-configuration, self-healing, and self-optimization. The self-configuration aspect of SON minimizes human intervention in network management. Configuration of the enhanced node based station (eNBS) is done by the self configuration algorithm. The eNB establishes a connection with the core network after detecting the core transport link. eNB performs self-testing and delivers a status report to the network management node. Self-configuration includes operational parameters, a neighbor cell list, and radio parameters. Self-optimization optimizes the network parameters during operation based on the measurements received from the network. There are many SON functions in Release 9. The most common are  
MLB: MLB manages the congestion of cells through mobility load transfer to other cells. As a result, MLB enhances the end user experience by achieving higher system capacity. MRO: MRO is an SON function that ensures complete mobility and proper handover in connected mode and cell reselection in idle mode. Inter-Cell Interference Coordination (ICIC): ICIC minimizes inter-cell interference using the same spectrum. The inter-cell interference is reduced with the coordination of physical resources from one cell to another. Random Access Channel(RACH): RACH optimization is achieved by adjusting the power control parameter or by changing the preamble format to reach the set target access delay. Coverage and Capacity Optimization (CCO): In Release 10, 3 GPP defined a new SON function named coverage and capacity optimization (CCO) to achieve an optimal trade-off between capacity and coverage. Energy Saving (ES): ES, defined in Release 10, targets the end user's quality of experience (QoE) by making the system energy efficient and environmentally friendly. Energy consumption optimization is achieved by designing network elements with low power consumption and temporarily shutting down unused capacity or nodes when not needed [67]. Self-healing stresses the maintenance of a cellular network. In cellular networks, faults are very common. Fault management is an important task in RANs. For any faulty situation, if the system cannot serve the network properly, degradation of the performance results, leaving the user with no service availability. This kind of problem results in severe revenue loss for the operator. Self-healing is therefore a key to fault management. We focus more on the three important mechanisms of self-optimization, self-healing, and self-protection. Self-optimization functions are used to analyze the performance of the network and automatically trigger the required actions.