# Self-Organizing Networks for 5G and Beyond: A View from the Top
## 3 SON Applications
### The Key SON Applications Leading to Full Automation  
#### 3.2.1 SON (ANR/Handover Success Rate) and NCL (The Need behind ANR)  
Prior to SON applications, academy and industry proposed solutions for automating key optimization activities for network performance KPI advancement, mainly related to the handover success rate and load balancing. The handover success rate enables continuous connection for User Equipment (UE) while subscribers are moving and it is one of the most critical KPIs for all mobile networks radio access technologies. A neighbor cell list (NCL) includes handover candidate cells and all UEs connected to a specific cell are informed about this candidate list [66]. More specifically, NCL includes interactions among:  
* Intra-frequency (same technology and same frequency) cells of the serving and neighbor cells.
* Inter-frequency (same technology and different frequency) cells of the serving and neighbor cells.
* Inter-RAT (different radio access technology) cells of the serving and neighbor cells.
* All the above cell interactions of the serving and neighbor cells in the same Base Transceiver Station (BTS)/NodeB/eNodeB/gNB.  
Before SON, neighbor cell lists (NCL) were manually configured by radio network planning and optimization engineers, by using data from the current and real-time status of the network (OSS) combined with data from coverage simulation tools to extract the optimal list. Apart from being time consuming and the need for operational expenses, this manual process includes multiple drawbacks, since it is very difficult to predict the radio propagation environment (at the microcell level) and predict all the actions that might take place such as the operation of new cells not activated during the manual NCL creation process or the non-operational status of specific cells at that time [67; 68]. In [69], the authors propose an automatic optimization algorithm based on base station coordinates and cell direction, but the disadvantage was that large lists of candidate cells were created, while the authors of [70; 71] were focused on finding potential neighbors based on cell coverage overlap.  
The prementioned proposals had a key contribution to the road towards self-optimization and their common disadvantage was that static network information data were mainly used as input (coordinates and cell coverage according to simulation tools). This creates difficulties in adapting in a constantly changing and dynamic network environment; thus, the authors of [68] further included live measurements for the creation of an NCL.  
The authors of [66] proposed a method for the self-configuration and self-optimization of NCLs. In the former case, the serving cells of the base stations collect signal quality information about the neighboring cells, while in the latter case real-time reports regarding neighboring cell quality measurements are collected from UEs and reported back to the serving cell. One of the key findings for the self-configuration scenario was that the neighbor's pilot signal quality cannot be measured in the whole serving cell coverage area since a sectorized antenna is used; thus, live measurements from the moving UEs are needed. A simulation deployed predicted an 85% call success rate for self-configuration and 97.3% for self-optimization. We believe that this result is optimistic; however, said paper is an excellent contribution to the explanation of self-optimizing and self-organizing rationale and a prelude to SON ANR applications.  
Figure 7 depicts the rationale of the self-configuration and self-optimization as discussed prior to the official launch of SON ANR applications.