In this paper,we present a user-complaint prediction system for mobile access networks based on network monitoring data.By applying machine-learning models,the proposed system can relate user complaints to network per...In this paper,we present a user-complaint prediction system for mobile access networks based on network monitoring data.By applying machine-learning models,the proposed system can relate user complaints to network performance indicators,alarm reports in a data-driven fashion,and predict the complaint events in a fine-grained spatial area within a specific time window.The proposed system harnesses several special designs to deal with the specialty in complaint prediction;complaint bursts are extracted using linear filtering and threshold detection to reduce the noisy fluctuation in raw complaint events.A fuzzy gridding method is also proposed to resolve the inaccuracy in verbally described complaint locations.Furthermore,we combine up-sampling with down-sampling to combat the severe skewness towards negative samples.The proposed system is evaluated using a real dataset collected from a major Chinese mobile operator,in which,events due to complaint bursts account approximately for only 0:3%of all recorded events.Re-sults show that our system can detect 30%of complaint bursts 3 h ahead with more than 80%precision.This will achieve a corresponding proportion of quality of experi-ence improvement if all predicted complaint events can be handled in advance through proper network maintenance.展开更多
基金This work was sponsored in part by the National Natural Science Foundation of China(Nos.91638204,61571265,61621091)。
文摘In this paper,we present a user-complaint prediction system for mobile access networks based on network monitoring data.By applying machine-learning models,the proposed system can relate user complaints to network performance indicators,alarm reports in a data-driven fashion,and predict the complaint events in a fine-grained spatial area within a specific time window.The proposed system harnesses several special designs to deal with the specialty in complaint prediction;complaint bursts are extracted using linear filtering and threshold detection to reduce the noisy fluctuation in raw complaint events.A fuzzy gridding method is also proposed to resolve the inaccuracy in verbally described complaint locations.Furthermore,we combine up-sampling with down-sampling to combat the severe skewness towards negative samples.The proposed system is evaluated using a real dataset collected from a major Chinese mobile operator,in which,events due to complaint bursts account approximately for only 0:3%of all recorded events.Re-sults show that our system can detect 30%of complaint bursts 3 h ahead with more than 80%precision.This will achieve a corresponding proportion of quality of experi-ence improvement if all predicted complaint events can be handled in advance through proper network maintenance.
