# Self-Organizing Networks for 5G and Beyond: A View from the Top
## 3 SON Applications
### The Key SON Applications Leading to Full Automation
#### 3.2.7. SON Coverage and Capacity Optimization (CCO), Interference Management and Adaptive Antennas  
SON Coverage and Capacity Optimization (CCO) and Interference Management applications were developed to ensure uniform coverage and capacity in problematic areas where continuous coverage cannot be ensured, signal quality is not satisfactory, and interference is a key issue that cannot be handled easily manually in real time. The aim of this is to extend a desired cell's coverage, mitigate coverage holes, and avoid interference at the same time. Triggered mainly by the high drop call rate, low handover success rates, and deteriorated QoS KPIs such as SINR (Signal to Interference Ratio), the specific applications operate mainly through antenna electrical tilt adjustments (adjusting azimuth and main or side lobes) centrally through the network OSS by controlling the Remote Electrical Tilt (RET) installed in each antenna [22; 23]. Antenna tilt changes (either down-tilting or up-tilting) must be performed very carefully since they affect coverage boundaries, while an interference issue might be triggered after such a change.  
* When electrical tilt is decreased (uptilt), the coverage area of the cell might overlap with a neighboring cell, and thus interference is created together with capacity issues. This specific case creates the so-called \"over-shooting cells\".
* On the other hand, when electrical tilt is increased (down-tilt), the coverage area of the cell is reduced, leading to interference mitigation towards the neighbor cells, traffic offloading because of capacity limitations, and the elimination of over-shooting cells. This tradeoff must be constantly monitored, and operators must perform such changes in a central manner through SON CCO application.  
Antenna parameter optimization needs constant monitoring; thus, it can be said that electrical tilt changes through SON and the algorithms supporting the specific applications need to react very fast in frequent load cases. Alternative methods include changes related to cell output power or reference/pilot signal power.  
Apart from central control through RET, another option is the usage of Active Antenna Systems (AAS), which are able to perform antenna beam forming or MIMO with spatial multiplexing, while the key behind the operational rational of AAS is to create spatial beamforming patterns [80]. This approach is expected to be a key technique for 5G networks due to the importance of massive MIMO for 5G development. Finally, the authors of [81]  
Figure 8: Conflicts between MLB and MRO.  
further analyzed the role of ML algorithms in antenna tilt changes and more specifically the role of reinforcement learning (RL) algorithms for obtaining the optimum result. The role of specific algorithms not only in the CCO case but SON as a system seems to present an ideal application scenario according to the upcoming conferences during 2022 on the issue, but in our opinion the use of deep reinforcement learning seems even more challenging and can bring even better results [82].