# Throughput Prediction of 5G Network Based on Trace Similarity for Adaptive Video
## 2 Related Works  
Network traffic prediction is a useful solution that can help with various challenges in networking such as resource provisioning, congestion detection, and fault tolerance. In the past two decades, researchers have shifted their focus from predicting Internet traffic generated by a range of applications to predicting single or multiple flows within mobile wireless networks. However, predicting single traffic flows in an unstable mobile environment presents a significant challenge due to the high variability and outliers compared to an aggregated trace smoothed by statistical multiplexing [7]. In addition, DASH systems that stream video segments in an ON-OFF fashion create bursts in transmission, leading to even more complex patterns. Traffic prediction involves using different models and techniques depending on the type of network and its purpose. In the past, researchers and network engineers used statistics-related techniques to analyse data patterns without prior knowledge or model training. These techniques are simple to use, have low computational overhead, and can handle a limited number of features. They usually compare the current data pattern with the last identified pattern to determine possible changes or predict future points in the time series based on the lagged data. However, these traditional methods cannot capture complex dependencies in large datasets and have limitations when dealing with non-stationary time series. Nowadays, machine learning (ML) methods are used in conjunction with or in opposition to traditional methods. However, there is still room for improvement in terms of efficiency, performance, computational burden, and training. There is also an ongoing debate about their accuracy and computational requirements [8]. A comprehensive overview of statistical and ML approaches to network traffic prediction can be found in [9]. In the past decade, deep learning models have made significant progress and have achieved remarkable success in various areas. With a sizeable and diverse dataset, machine learning algorithms can learn to recognize intricate non-linear patterns and discover unstructured relationships without having to make assumptions ahead of time. Thismeans that ML algorithms are not constrained by preconceived notions or predetermined data-generating procedures, which allows the data to reveal their insights. However, the superiority of ML is not apparent when it comes to network traffic forecasting, as some research has reported that feed-forward ANNs and ARIMA could provide better performance than deep ANNs [10]. The literature on network throughput prediction is extensive and can be classified into several domains. Firstly, we can distinguish between low and high time aggregation scales for the network traffic. The boundary between the scales is subjective, and there are works that set the prediction horizon to milliseconds [7; 11], seconds [3], minutes [12], and hours [13]. We focus on the seconds scale, where the LSTM ANN and its variant are often selected for prediction. Most works have focused on higher aggregation scales because the prediction is simpler, as the traffic is smoother, has seasonal components, and has fewer outliers. An example of a work incorporating low aggregated traffic is [14], which analysed downlink and uplink throughput generated by remotely controlled vehicles in LTE and 5G networks. Besides LSTM networks, another popularly employed method for the low aggregation scale is Random Forest. As per the study conducted by [PERSON] et al. [15], the Random Forest model is more effective in flow throughput prediction as compared to a deep ANN. The study tested various time windows ranging from 0.03 s to 4 s on nearly one million flows consisting of traces from various networks and applications. In addition to flow history data, the study also considered other attributes related to TCP such as window size, window scale, and segment retransmissions. Secondly, prediction can also be a single step, e.g., [16], or multiple steps, e.g., [13].