# Throughput Prediction of 5G Network Based on Trace Similarity for Adaptive Video
## 1 Introduction  
Cellular networks have been improving rapidly over the last few decades. The latest 5G systems are designed to offer faster data transfer speeds and lower network latency than earlier generations. However, some experts say that limitations in the underlying technology, including devices and wired internet access, may reduce these potential benefits [1]. Additionally, current wireless protocols are not optimized for the unique conditions of cellular networks, leading to the underutilization of available bandwidth [2]. This can be particularly problematic for multimedia applications like dynamic adaptive streaming over HTTP (DASH), where demand often exceeds available resources. The predictability of network throughput can improve the performance of upper-layer protocols and network applications. In the abovementioned DASH, a video player periodically downloads segments, concatenates them, and delivers a continuous video stream to the user. During video playback, the player can request different segment versions to adjust the bit rate based on predicted network conditions. If the prediction is not accurate, the streaming algorithms will either be too conservative and download a lower-quality video than necessary or be too aggressive and download a video of higher quality, which will result in the video freezing at a certain time point in the future. Therefore, the more accurate the prediction, the better the quality of the user's experience [3]. Typically, a DASH application measures the throughput after downloading each video segment. Hence, the video player decides on the video quality at intervals longer than the length of a downloaded video segment, which is usually between 2 and 10 s of video. For example, the Microsoft Smooth Streaming algorithm uses 2-5 s [4]. Therefore, to make our results suitable for this type of service, we tried to predict the average throughput in the next 4 s, with a trace of historical throughput measured every 4 s.  
The variability of wireless transmission, such as changes in signal strength, interference, noise, and user movement, can result in highly unpredictable data transfer rates. The radio environment is dynamic, and even small changes in distance can significantly alter its properties [5]. Because of this, the throughput of wireless networks can fluctuate rapidly due to various random factors, including environmental conditions. Traditional models such as ARIMA cannot accurately predict the relationship between these factors and throughput. Additionally, prediction algorithms based on ANNs may not outperform simpler models unless the underlying data are extensive enough to fit complex models. Even if we capture sufficient amounts of past network throughput, its statistical properties and patterns can be altered by, e.g., a changing user equipment (UE) location, other UE joining the same cell, moving objects, or changing weather [6]. In this environment, the predictive model struggles with non-stationarity and concept drift. It repeatedly needs to reformulate its decision boundaries, to the detriment of the prediction accuracy. Our contribution is to overcome this shortcoming by the following:  
* Building separate models for the groups of times series representing the throughput;
* Constructing automatically the subgroups using similarities in the trace measured by a moving average and a variance. Contrary to the works employing clustering and prediction, we do not compare or group multiple traces but search for similarities within each trace separately. Also, contrary to other research, to cluster the traces, we do not compare them directly but use for this purpose their statistical features. Then, we train separate LSTM ANNs for each group individually. When predicting a new value, before we feed data into an LSTM unit, we select the model that best matches the characteristics of the input vector. In the Section 2, we describe the problems of network traffic prediction with examples from selected works.