# A Modular Web-Based Software Solution for Mobile Networks Planning, Operation and Optimization
## 3 Radio Access Network Algorithms
### Network Optimization  
When network performance degradation is detected, optimization actions should be taken to restore or improve the desired QoS levels. In this work, two algorithms are considered for the optimization of antenna physical characteristics, the crossed sector/feeder detector and the antenna tilt optimization algorithm. Finally, the handover algorithm is considered for the optimization of handover parameters. Concretely, the crossed sector/feeder algorithm is designed to solve a frequent network implementation error, which is to erroneously swap the antenna cables of two sectors, when deploying BSs, thus creating a crossed sector/feeder scenario. Even though the two swapped sectors/antennas still provide RF coverage, they do not illuminate the desired cell area, thus causing several potential performance issues mostly related with interfer  
Figure 2: Flowchart for the overshooting, coverage holes and pilot pollution algorithms. ence. The algorithm considers the received power (measured by DTs) and the BS antenna radiation pattern. The concept is to compare the measured received signal strength from two sectors, with a signal strength prediction based on a correct network topology and antenna pattern knowledge [23]. The antenna tilt algorithm aims to optimize low coverage and high interference areas, through the adjustment of the antenna tilts, using DT measurements and antenna models. The process is achieved using a specific implementation of a Particle Swarm Optimization (PSO) algorithm that selects the optimal tilt value according to a multi objective loss function, considering the cell coverage and/or the cell interference [22]. The handover optimization algorithm aims to optimize the handover parameters, either facilitating or delaying handovers in order to maintain the highest QoS for the UEs. This is achieved by using both dedicated and scanner DT measurements, comparing RF conditions (power and interference) monitored by the UE in dedicated mode, with the overall RF conditions, including potential cells that the UE did not connect to, but can sustain better performance. This analysis can identify scenarios were cells offering potential higher QoS were available, and a handover did not occur, given the configured handover parameters. By optimizing these parameters, new handovers can be feasible thus achieving an overall higher QoS [24]. Both the antenna tilt and the handover optimization algorithms share a common structure, which is presented in the flowchart of Figure 3.  
From the flowchart of Figure 3, it can be seen that the algorithms, from the respective cell footprint (DT measurements), use a particular objective function, which is minimized using a PSO algorithm to identify new configurations. A new configuration updates the cell footprint (e.g., the received power with a new tilt configuration), and the process repeats itself until a maximum number of iterations is reached. The objective function for the antenna tilt optimization algorithm is given by (adapted from [22]):  
\[f_{A}(\Omega)=C(\Omega)+M(\Omega) \tag{1}\]  
Figure 3: Flowchart for the antenna tilt and handover optimization algorithms. where \(\Omega\) is the cell tilt configuration (Mechanical Downtilt (MDT) and Electrical Downtilt (EDT)), \(M(\Omega)\) is a function that penalizes configurations \(\Omega\) very distinct from the cell initial configuration, and \(C(\Omega)\) is the main optimization objective given by:  
\[C(\Omega)=\alpha_{1}C_{CH}(\Omega)+\alpha_{2}C_{OS}(\Omega)+\alpha_{3}C_{PP}(\Omega) \tag{2}\]  
where \(CH(\Omega)\), \(C_{OS}(\Omega)\), and \(C_{PP}(\Omega)\) indicate the cell footprint performance in terms of coverage holes, overshooting, and pilot pollution, respectively. The coefficients \(\alpha_{1}\), \(\alpha_{2}\), and \(\alpha_{3}\) can be adjusted to prioritize any of the terms.