# Throughput Prediction of 5G Network Based on Trace Similarity for Adaptive Video
## 2 Related Works
Thus, a web application cannot employ these measurements directly unless the future mobile networks go beyond the network-focused paradigm and start to push load information to UE [22]. In most surveyed studies, the authors preprocessed the traffic traces before passing them to the prediction model. For this purpose, they used various techniques such as standardization and data scaling, including min-max normalization. Sometimes, researchers have applied more elaborate preprocessing. One approach is to break down a network trace into smaller parts and apply a distinct model to each part. This helps to avoid model degeneration and leads to better prediction accuracy. For example, if a network trace has seasonal components, the preprocessing separates them and applies the predictive model to each separately. The final prediction combines the results generated by these separate models. The decomposition technique can be used in either the frequency or spatial domain. The authors of [26] employed discrete wavelet transform, in [27] tensor completion was used, while the authors of [28] applied Fourier analysis. In [26], a traffic trace was decomposed into two components using a discrete wavelet transform: one with high frequencies and another with low frequencies. Various prediction models can be utilized based on the statistical properties of each component. In [28], the cellular traffic series was decomposed into three components and further modelled with three different models. For the largest periodic component, the least-squares method was used, while the middle component and residual were modelled using LSTM and Gaussian process regression, respectively. The authors of [29] investigated wireless traffic on an hourly scale. From the original series, they extracted a fluctuation pattern, which they called the baseline feature, while retaining the noise component, called the residual feature. The fluctuations corresponded to a daily scale and were analysed in the context of weekends and working days. The patterns were identified by hierarchical clustering, while for the prediction a bidirectional LSTM network was used. Clustering is often used in the case of spatio-temporal traffic models where BSs form groups based on either the geographical (spatial) location or temporal behaviour of transmitted traffic. However, the spatial clustering indirectly assumes that the geographical location of a BS influences the properties of traffic in this BS. The research employing this concept focuses on high aggregated traffic, measured in hours or days, and aims at the performance optimization of 5G network infrastructure. For example, in [30], the authors clustered the traffic coming from multiple BSs to forecast the load in the network. The authors of [31] used a convolutional ANN (CNN) combined with LSTM for clustered traffic prediction. The CNN model received input sequences and extracted significant features. Meanwhile, the LSTM network captured the short-term and long-term dependencies of temporal features. Overall, the presented categorization is far from complete and extensive but simultaneously shows how broad the topic of throughput prediction is. There are comprehensive surveys available that provide an in-depth analysis of various ML models presented and evaluated by the authors, e.g., [32; 33]. Regarding the abovementioned criteria, in our work, we apply an LSTM ANN and implement a single-step, short-time prediction of 4 s. Our prediction uses only the network throughput measured at a UE device. Table 1 summarizes the abovementioned criteria of our predictive model compared to other research. As was mentioned in Section 1, due to the characteristics of the wireless environment, network throughput is not stationary and changes its statistical properties with time, leading to so-called concept drift [34]. Concept drift occurs when the underlying data distribution changes over time.