# Crowdsourcing Based Performance Analysis of Mobile User Heterogeneous Services
## 6 Conclusions and Perspectives  
The crowdsourcing approach offers a new cheap paradigm for services' quality of experience (QoE) assessment perceived by end users. Analyzing traces is very useful for enhancing the QoS and identifying the root cause of poor performance that may happen in some small zones. It is also crucial for operators to easily produce coverage maps, for instance, to demonstrate that the coverage commitments on which the license is conditional have been met in addition to limiting customer churn due to quality dissatisfaction. We have collected a dataset for three popular Internet services using two different \(3G/4G\) user terminals. The measurements are effectuated during 6 months in 2021 and for one popular French operator in a large region in France (a rectangle 96 km \(\times\) 68 km). This region is divided later on the map into 1024 small zones. The QoE in terms of user's Mean Opinion Score (MOS) has been computed from known models found in the literature for every service with the aim of analyzing the cause of poor performance found in some zones. Several problem events are defined and matched against the traces. Our analysis is applied on both plain-text and encrypted traffic within different mobility modes. We concluded that the radio provisioning is not the only possible cause of poor performance as anyone intuitively thinks especially with mobility. The capacity of application's servers, their location with respect to users, and the user terminal characteristics can be good reasons for problems. We have noticed also that older mobile technologies are still used to enlarge the coverage in less dense sectors (where the density of population per km\({}^{2}\) is not important). We have also demonstrated that the key radio parameters can be used in a simple way to give an acceptable prediction of the HD video quality metrics, mainly the launch time, the bitrate and the MOS. It is worth mentioning that our study is applied for 5G new radio. The crowdsourcing campaign, the collecting and preparation of the datasets and the applied performance evaluation methodology on the key internet services are still the same. The main difference from the practical point of view is changing the dataset features with the new radio indicators. In fact, 5G will lead to significant gains in network throughput, outage and power consumption thanks to several key technologies including Downlink/Uplink Decoupling, Massive MIMO (beamforming), and the introduction of millimeter wave bands [33; 34]. Due mainly to beamforming, the 5G new radio (NR) uses Synchronization Signals (SS) and Channel State Information (CSI) instead of the Cell-Specific Reference signals (CRS) which are transmitted by neighboring cells [35]. In fact, in \(3G/4G\) systems, the CRS is shared by all User Equipment (UE) in the same cell and this is why a CRS transmission cannot be beamformed to a specific UE. Instead, in the 5G NR, a UE specifically configured and dedicated measurement signal named the Channel State Information Reference Signal (CSI-RS) had been introduced since release 10. Later, configuring multiple CSI-RS to one UE simultaneously is enabled with a larger number of antenna ports (e.g., release 13). This permits measuring the characteristics of a radio channel so that it can use correct modulation, code rate and beamforming. The base station (e.g., gNB) sends CSI Reference signals to report channel status information such as CSI-RSRP, CSI-RSRQ and CSI-SINR for mobility procedures [35]. Therefore, our study is applied to 5G networks once the correct CSI Reference signals are collected instead of those for \(3G/4G\). In the future, we would like to explore other efficient ensemble ML methods and deep learning techniques that can be used to achieve real-time measurement of video QoE. Software, writing-review and editing, L.A.; Supervision, validation, writing-review and editing, A.D. All authors have read and agreed to the published version of the manuscript. This research received no external funding.