# Throughput Prediction of 5G Network Based on Trace Similarity for Adaptive Video
## 2 Related Works
Multistep analysis is more challenging and is usually applied to forecast more aggregated traffic with a longer time horizon. In this paper, we use single-step prediction. Multistep prediction would be more beneficial, as a DASH system could make more far-fetched decisions and avoid too frequent changes in video quality. However, such a prediction would be very challenging and subjected to a significant error. Thirdly, many works have considered the spatial characteristics of mobile networks, including traffic generated by multiple base stations (BSs). This approach aims to enhance traffic prediction by exploiting the correlation between traffic flows across neighbouring BSs generated by users' continuous movement within the network [17; 18; 19]. The spatial modelling of traffic flow has several challenges. One issue is data collection. The point of the network at which to capture the traffic in order to obtain the characteristics of the traffic flow in the system is an open question. It is important to note that relying on a few fixed measurement points may not accurately capture the radio environment at different locations [20]. The radio environment can vary significantly within just a few meters, so it is unclear how reliable these measurement points are in predicting throughput for mobile devices that frequently change radio environments [21]. While monitoring multiple UE devices can help, it also has limitations. For example, reporting UE geographic coordinates raises privacy concerns, and transmitting radio measurements from a UE to a BS would increase system traffic, leading to less accurate predictions. Many works have considered both upper- and lower-layer information when predicting link bandwidth, as relying solely on historical throughput can result in inferior performance. Using all available features is known as multivariate prediction, while only considering a single feature, such as throughput, is univariate prediction. Therefore, combining multiple features is crucial for accurate link bandwidth prediction. From the statistical point of view, the approach that considers only a single feature, e.g., throughput, is univariate prediction, while using more features is multivariate prediction. Therefore, the fourth criterion concerns whether authors use only application layer data or support their models with network layer information. Due to the better prediction accuracy, many works fall into the latter category. For example, the authors of [22] presented a cooperative data rate prediction technique that incorporates information from the client and network domains. They used a Software-Defined Radio (SDR)-based control channel sniffer to emulate the behaviour of possible network-assisted information provisioning within future 6G networks. The results indicated that the proposed cooperative prediction approach could reduce the average prediction error by up to 30%. The study in [23] analysed correlations between lower-layer data and link bandwidth in cellular networks. The authors then utilized a machine-learning-based prediction framework to identify important features and forecast link bandwidth. In [24], the authors predicted the bandwidth, firstly recognizing the type of network to which a UE was connected: 3G, 4G, or 5G. They also used a handover detection algorithm, which caused the bandwidth variance. The researchers applied a dedicated bidirectional LSTM model for each type of network. [PERSON] et al. in [25] compared a few predictive models, including Support Vector Machines, Random Forest, and an LSTM ANN. Using data gathered from the signal-to-noise ratio (SNR), channel quality indicator (CQI), reference signal of received quality (RSRQ), and device velocity, the authors stated that these cell-level measurements improved throughput prediction accuracy compared to solely throughput-based prediction. However, the network-side information is unavailable for application-level protocols like DASH, which relies on measurements gathered by a client-side video player.