Abstract: A method, device, and apparatus for optimizing unbalanced loads covered by a same network and a storage medium are provided. The method includes: determining whether loads of cells covered by a same network are balanced; determining, if the loads of the cells are unbalanced, a cell with a highest network load rate and a cell with a lowest network load rate in the cells; determining a target offset parameter corresponding to a predetermined index according to a timing advance and the number of sampling points of each of the cell with the highest network load rate and the cell with the lowest network load rate; and adjusting a load of the cell with the highest network load rate and a load of the cell with the lowest network load rate based on the target offset parameter.

Abstract: A method, device, and apparatus for optimizing unbalanced loads covered by a same network and a storage medium are provided. The method includes: determining whether loads of cells covered by a same network are balanced; determining, if the loads of the cells are unbalanced, a cell with a highest network load rate and a cell with a lowest network load rate in the cells; determining a target offset parameter corresponding to a predetermined index according to a timing advance and the number of sampling points of each of the cell with the highest network load rate and the cell with the lowest network load rate; and adjusting a load of the cell with the highest network load rate and a load of the cell with the lowest network load rate based on the target offset parameter.

Abstract: Neighbor cell planning method and device based on Thiessen polygons are provided. The method includes acquiring working parameter data of cells; adjusting longitudes and latitudes of the cells based on the working parameter data; constructing a Delaunay triangulation based on the adjusted longitudes and latitudes of the cells; generating Thiessen polygons based on the Delaunay triangulation; and calculating neighbor cells of each cell based on the Thiessen polygons and exporting a list of neighbor cells for each cell.

Abstract: Disclosed is a method for implementing antenna azimuth correction based on user data. Five prediction algorithms are combined to avoid some defects of an individual algorithm. By means of a method for applying a plurality of prediction model weights, a plurality of algorithms is organically combined, a high weight is applied to an algorithm having a good prediction effect, and a low weight is applied to an relatively bad algorithm, such that not only the accuracy of data prediction but also the stability of data prediction for different regions are ensured. By drawing a grid-level user data RSRP heat map for analyzed data to obtain sampling point information around a cell and a predicted azimuth, the present invention can find out, by means of deeper analysis, problems in engineering parameters, such as a longitude/latitude error, an azimuth error, and a reversely connected cell.

Abstract: A method and system for detecting abnormal values in an LTE network is provided: dividing measured data into a training and a testing set; defining clusters and parameters in the training set, and finding the cluster to which each point belongs using clustering algorithms; calculating a likelihood of each point based on parameters and clustering results; assigning the likelihood into an abnormal, an intermediate or a normal region according to a set warning and alarming threshold; and applying a calculated model to the testing set, the likelihood of each point is calculated and assigned to a region, thereby finding abnormal values in the testing set. The variation of data points versus time may be better understood by introducing time axes into the model, thereby multiple abnormal values may be discovered from a sequence of multiple points. The method can immediately detect abnormal values and the error rate is low.

Abstract: Disclosed is a method for implementing antenna azimuth correction based on user data. Five prediction algorithms are combined to avoid some defects of an individual algorithm. By means of a method for applying a plurality of prediction model weights, a plurality of algorithms is organically combined, a high weight is applied to an algorithm having a good prediction effect, and a low weight is applied to an relatively bad algorithm, such that not only the accuracy of data prediction but also the stability of data prediction for different regions are ensured. By drawing a grid-level user data RSRP heat map for analyzed data to obtain sampling point information around a cell and a predicted azimuth, the present invention can find out, by means of deeper analysis, problems in engineering parameters, such as a longitude/latitude error, an azimuth error, and a reversely connected cell.

Abstract: A method for dynamically selecting an optimal model by three-layer correlation for predicting a large amount of data is provided, which includes a prediction model algorithm library, a weightage algorithm library and an ensemble learning algorithm with optimal weightage. The prediction model algorithm library comprises multiple prediction model algorithms which are called a common interface at the lowest layer of the correlation algorithm, to provide a prediction function and a support function for upper layers. The weightage algorithm library covers a diversity of underlying algorithms of the prediction algorithm library, and selects and combines the underlying algorithms with multiple methods based on prediction results from the underlying algorithms to form multiple weightage algorithms. The ensemble learning algorithm with optimal weightage is used to select an optimal weightage algorithm for prediction based on evaluation of the weightage algorithm on a validation set.

Abstract: A crowdsourcing-mode-based analysis method for utilization of wireless network resources by mobile applications (Apps) includes the following steps. Behavior characteristic data of each type of mobile App is collected by using a data collection tool, which is installed on a mobile client and based on a crowdsourcing technology and an analysis algorithm located on a cloud server, and using a machine learning algorithm targeted to the behavior characteristic data. A three-stage two-layer associated mapping model is established among a characteristic behavior of the mobile application, wireless network traffic, and wireless network resources, and quantitatively analyzing, in a time dimension, how each mobile application service in a mobile communications network consumes wireless resources in a cell.