Cellular QoE Prediction for Video Service Based on Causal Structure Learning

With the development of telecommunication technology and the popularity of intelligent devices, user experience in cellular network has become the primary factor of concern. At the same time, network operators are plagued by two problems: Prediction of users real-time experience and find network parameters that have a decisive impact on user experience. We proposed a novel scheme for user experience prediction to deal with these two problems. Causal structure learning for cellular networks was used to analyze numerous performance indicators (KPIs) collected from base stations and key quality indicators (KQIs). Through causal structure learning, a directed causal graph based on the association between KPIs and KQI can be obtained. This causal structure can be embedded in graph attention network. Among them, attention mechanism was selected to further strengthen the correlation between parameters. This correlation between each KPI and between KPIs and KQI was used to predict future value of cell level user experience. Results showed that proposed method performance well in cellular network data analysis and user experience prediction.