# Optimizing the Quality of Service of Mobile Broadband Networks for a Dense Urban Environment
## 3 Experimental Design  
This paper adopted a repeatable and thoroughly scrutinized methodology for data measurement, collection, and analysis, and trends were identified where obvious. This section details the campaign strategies, equipment, materials, and algorithms used in the sorting and optimization of broadband KPIs of interest. We will keep in mind that while a holistic approach was used in the acquisition of the data, a more selective approach was used in determining the KPIs to be analyzed for trends, patterns, and those optimized using the PSO algorithm. For the more pragmatic application of the optimized metrics obtained, specific areas of Lagos, Nigeria, and a commercial MNO with nationwide coverage (Etisalat) have been targeted for the execution of the measurement, experiment, and optimization procedures. These methods can subsequently be adopted in other geographical areas and MNOs for the effective analysis and optimization of the metrics which are closely knitted to the user's experience and the quality of service of the mobile network. The campaign involved measurements obtained with a personal computer equipped with GENEX Assistance Version 16, a standalone software program with its accompanying Geneva version 16 probe for obtaining different network KPIs. A GPS unit was also necessary to determine the distance between test sites (stations). For accuracy, a 4G compatible Huawei Modern E392 with LTE upload speed up to 50 Mbit/s and download speed up to 100 Mbytes/s is used at a moderate speed of 30 km/h. A moderate speed ensures uniformity in the results obtained from the test terminal station in the test car. Data measurements were recorded against the global positioning system (GPS) coordinates (longitude and latitude), and the DT assistant also logged the time. Once the DT was completed and the data logged into the CSV files for the three locations, the viability of the data was then checked with a bird's eye view for obvious discrepancies and non-conformity with threshold values for the KPIs obtained. Though there was no need for a test rerun, as the data held substantial quality to pass the viability test, it was important to establish sets of rules to ensure the data obtained could efficiently be used to represent the network conditions of the MNO in the areas considered. Once the data viability test was passed, the data was then imported into MATLAB for an extensive statistical analysis to observe the measure of dispersion and correlation in the network quality obtained at the different locations. As shown in Tables 2-4, the relevant measures of dispersion, such as the range, median, standard deviation, and variance, show the distribution characteristics of the data obtained. The skewness factor is also used to show the asymmetry of the measurement and further establish the viability of the DT. The KPIs are graphically interpreted and monitored at the next stage for signal levels and operational intricacies. As a means of ensuring the efficiency of the methodology, the signals for each KPI are compared for the different areas considered. From this process, the QoS and bandwidth strength at different locations of the DT can be shown, and positions with the least network performance can be noted for subsequent optimization procedures. Starting from the experimental planning, DT, the MATLAB simulation, and analysis of the data obtained, the optimization process holds a high level of relevance, and care is taken to write the PSO algorithm, specify the upper and lower boundaries, and seek ways to ensure that swarm intelligence is used in its purest form to ensure accuracy of the optimization solution derived from the objective function chosen. The RSRP and SINR are used to develop the objective functions and boundary conditions for the optimization process. Optimized solutions are then tested against industry-recommended values to ascertain their practicality and usefulness to the telecommunication industry. Figure 1 is a flowchart representation of the methodology.