# Throughput Prediction of 5G Network Based on Trace Similarity for Adaptive Video
## 4 Experiment and Results
### Discussion  
The results show that the ensemble of LSTM ANNs could improve prediction accuracy, especially for traffic generated by a mobile UE device. This finding can be explained in two ways: either the clustering was better, or the clustered data provided to the LSTM model was easier to predict. When considering the former case, the reason for better clustering may have its roots in the mobility pattern of the M1 and M2 traces. In the case of the M2 trace, the UE moved between several places, creating a closed trajectory, which was repeated multiple times. This mobility pattern probably introduced a certain amount of regularity into the trace. The clustering algorithms captured these recurring patterns; however, the same patterns negatively impacted the training of the universal LSTM and MA(1) models. Considering the latter case, some fragments of the UE's movement path may have had a better quality signal (measured as CQI), which resulted in stable and more predictable throughput. Thus, some models may have achieved significantly better prediction accuracy, and, consequently, they lowered the NRMSE for the whole ensemble. In such a situation, the universal model would also have an adaptation problem when switching repeatedly between relatively stable and irregular trace parts. Unlike in the fixed scenario, the clustering algorithms impacted the result. For both M1 and M2 traces, AutoClass achieved the lowest NRMSE, followed by OPTICS and KMeans. A possible reason for this could be that the clusters generated in the mobile scenario were more specific than those generated in the fixed scenario. With the more diverse groupings, the performance differences among the clustering methods were more visible, and more elaborate algorithms achieved better scores. Although the prior clustering improved the prediction, the error for the traces generated by a mobile UE device was higher than for the traces from a fixed UE device. This means that the proposed solution could not transform a complex traffic trace into several groups containing simpler traces. After all, the mobile scenario was not a concatenation of multiple fixed scenarios but contained time segments where the UE was on the move. The methods applied, namely feature extraction, clustering, and the LSTM ensemble, were sufficient to give a better result than a single LSTM network. Each of these methods requires more tuning to work optimally, but they should also cooperate with the remaining elements of the prediction workflow. Thus, our work leaves several open questions. Firstly, which features should be selected to obtain the best clustering? Although feature extraction methods are generic, extracted features often depend on the application context. This means that one set of features that is significant for one time series may not be important to another. The discovery of patterns in data is rarely a one-shot action but  
Figure 11: NRMSE of different prediction models for a mobile UE device generating M1 and M2 traces. rather a process of finding clues, interpreting the results, transforming or augmenting the data, and repeating the cycle. Therefore, another feature selection step is sometimes needed to limit the number of feature dimensions. Although the importance of the selection can be determined using built-in features of the _tsfresh_ algorithm or principal component analysis, these most important features do not necessarily guarantee the best clustering results. Secondly, what is the optimal number of clusters to divide the traces and result in the best LSTM performance? There are many measures of clustering performance, but do their results translate automatically into a better ensemble of models? Therefore, it might be worth exploring the algorithms that utilize time series information in more detail. Thirdly, what would be the benefit of localized prediction in the case of more sophisticated prediction models?