Automating configuration management in cellular networks
Methods and apparatuses for automating configuration management in cellular networks. A method of a UE comprises: training, based on historical samples, a regression model y using samples obtained from a set of parameters, wherein the regression model y comprises a function of a first term X and a second term h; and predicting, based on the regression model y, a target KPI to capture parameter impacts corresponding to the second term h.
1 . A network entity in a communication system, the network entity comprising:
memory; and
a processor operably connected to the memory, the processor configured to:
train, based on historical samples, a regression model y using samples obtained from a set of parameters, wherein the regression model y comprises a function of a first term X and a second term h, and
predict, based on the regression model y, a target key performance index (KPI) to capture parameter impacts corresponding to the second term h.
2 . The network entity of claim 1 , wherein the processor is further configured to:
determine whether each of samples is balanced among the set of parameters; and
perform an oversampling operation based on a determination that each of samples is imbalanced.
3 . The network entity of claim 1 , wherein the first term X is a regressor that is a selected factor from a performance management (PM) counter and the second term h is a proxy variable, the first term X being a non-tunable variable.
4 . The network entity of claim 1 , wherein the processor is further configured to predict an output of the regression model y based on a long-term cell operation.
5 . The network entity of claim 1 , wherein the processor is further configured to:
identify a set of distinct parameters P i each of which corresponding to the second term h;
identify a set of regression models y ij corresponding to each of the set of distinct parameters P i ; and
compute a median based on the set of regression models y ij .
6 . The network entity of claim 1 , wherein the processor is further configured to:
select, based on a machine learning (ML) operation, a set of KPIs from data of performance management (PM); and
remove, from the set of KPIs, a KPI including causality with a configuration management (CM) setting for training the regression model y.
7 . The network entity of claim 6 , wherein the processor is further configured to:
compute a Pearson correlation coefficient for the target KPI; and
generate, based on the Pearson correlation coefficient, a ranking list including factors that are related to the target KPI.
8 . The network entity of claim 1 , wherein the processor is further configured to:
construct a classification model using a machine learning (ML) algorithm;
determine whether configuration management (CM) is changed;
determine whether a KPI is degraded based on a determination that the CM is changed; and
perform, based on the classification model, a diagnosis to determine whether a parameter misconfiguration causes a degradation of the KPI.
9 . The network entity of claim 8 , wherein the processor is further configured to:
compute first anomaly samples after changing of the CM and second anomaly samples before changing of the CM; and
generate, based on the first anomaly samples and the second anomaly samples, a relative metrics and absolute metrics.
10 . The network entity of claim 9 , wherein the processor is further configured to:
compute, based on the relative metrics and the absolute metrics, average metrics;
generate, based on the average metrics, a mapping function; and
generate, based on the mapping function, output labels using a threshold, the output labels comprising an unlikely, an undecided, or a likely.
11 . A method of a network entity in a communication system, the method comprising:
training, based on historical samples, a regression model y using samples obtained from a set of parameters, wherein the regression model y comprises a function of a first term X and a second term h; and
predicting, based on the regression model y, a target key performance index (KPI) to capture parameter impacts corresponding to the second term h.
12 . The method of claim 11 , further comprising:
determining whether each of samples is balanced among the set of parameters; and
performing an oversampling operation based on a determination that each of samples is imbalanced.
13 . The method of claim 11 , further comprising predicting an output of the regression model y based on a long-term cell operation, wherein the first term X is a regressor that is a selected factor from a performance management (PM) counter and the second term h is a proxy variable, the first term X being a non-tunable variable.
14 . The method of claim 11 , further comprising:
identifying a set of distinct parameters P i each of which corresponding to the second term h;
identifying a set of regression models y ij corresponding to each of the set of distinct parameters P i ; and
computing a median based on the set of regression models y ij .
15 . The method of claim 11 , further comprising:
selecting, based on a machine learning (ML) operation, a set of KPIs from data of performance management (PM); and
removing, from the set of KPIs, a KPI including causality with a configuration management (CM) setting for training the regression model y.
16 . The method of claim 15 , further comprising:
computing a Pearson correlation coefficient for the target KPI; and
generating, based on the Pearson correlation coefficient, a ranking list including factors that are related to the target KPI.
17 . The method of claim 11 , further comprising:
constructing a classification model using a machine learning (ML) algorithm;
determining whether configuration management (CM) is changed;
determining whether a KPI is degraded based on a determination that the CM is changed; and
performing, based on the classification model, a diagnosis to determine whether a parameter misconfiguration causes a degradation of the KPI.
18 . The method of claim 17 , further comprising:
computing first anomaly samples after changing of the CM and second anomaly samples before changing of the CM; and
generating, based on the first anomaly samples and the second anomaly samples, a relative metrics and absolute metrics.
19 . The method of claim 18 , further comprising:
computing, based on the relative metrics and the absolute metrics, average metrics;
generating, based on the average metrics, a mapping function; and
generating, based on the mapping function, output labels using a threshold, the output labels comprising an unlikely, an undecided, or a likely.
20 . A non-transitory computer-readable medium comprising program code, that when executed by at least one processor, causes a network entity to:
train, based on historical samples, a regression model y using samples obtained from a set of parameters, wherein the regression model y comprises a function of a first term X and a second term h, and
predict, based on the regression model y, a target key performance index (KPI) to capture parameter impacts corresponding to the second term h.