# Self-Organizing Networks for 5G and Beyond: A View from the Top
### Motivation and Main Drivers for SON Deployment in Next-Generation Networks  
The SON concept applied in networking and the telecommunications industry resulted from a long history of original academic research, through industry-academia consortia and standardization organizations, resulting in network automation techniques and leading operators and equipment manufacturers to apply it as the basic solution for automating network functions and optimization processes, especially in the radio access domain. SON platforms are already being used with great success, leading to network performance and Key Performance Indicator (KPI) improvements as well as OPEX/CAPEX benefits in 2G/3G/4G radio access networks of mobile operator networks; interest has been increasing significantly in the last few years as 5G network deployments gradually expand and operators start switching off 2G and 3G networks in order to migrate the frequency usage to 4G and 5G network structures [9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 25]. The basic trigger point for the development of SON systems is closely related to the automation benefits of ML and artificial intelligence (AI) techniques in order to overcome the manual handling of very complex and real-time aspects of network management, planning, and optimization procedures. As an example, consider the existing scenario for real mobile infrastructures where an operator intends to expand or modernize an existing multi-technology network either in dense urban or rural area cell clusters. During this long process, new base stations (macro, micro, pico, and femto cells and repeaters) enter or leave the network and relocate, and daily issues, such as low-quality channels or interference, must be mitigated manually. Prior to SON, the first step in this process was to manually configure the basic RAN, transport, and core network parameters, while at the same time redesigning the neighboring area network. If we include as a factor the unpredictable user mobility and activity, it is clear that the manual handling of all these procedures has very high complexity and is time consuming and error prone [26]. These challenges can be addressed by SON platforms/systems since network parameter tuning and optimization can be performed automatically through intelligent SON algorithms in real time. This is achieved through decisions made by the SON platform algorithm, which takes real-time network data as input (e.g., network counters collected by various network entities, such as the radio access network controllers--Base Station Controller (BSC), Radio Network Controller (RNC), Mobility Management Entity (MME), and Access and Mobility Management Function (AMF), for 2G, 3G, 4G, and 5G, respectively--at various granularities). It can be said that as networks evolve following the network deployment lifecycle with procedures starting with their initial planning, network design, installation and commissioning, optimization and finally maintenance, only some sub-procedures such as site survey, selection, licensing, equipment shipment and installation or hardware replacement cannot be substituted by SON platforms [16; 27]. Additionally, especially considering the RAN optimization aspect, which is our focus, the following figure depicts the comparison of a traditional, legacy (manual) approach compared to automated optimization through SON. The benefits, as shown in Figure 1, are important since the conventional radio access network optimization process takes approximately 8 weeks, which is slower than the automated approach. More specifically, the conventional optimization rationale includes three basic steps that have to be repeated manually many times until the optimum result is achieved. In order to optimize a RAN, the first step is to collect data from drive tests and measurement campaigns, KPIs, customer complaints and existing configuration. Step 1 is the input to step 2 that includes the reconfiguration and optimization steps so that network performance is improved.